IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
The main purpose of data mining is to extract knowledge from large amount of data. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. This paper presents the application of hybrid model for stroke disease that integrates Genetic algorithm and back propagation algorithm. Selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In addition the hybrid model that leads to further improvised categorization, accuracy compared to the result produced by genetic algorithm alone. In this study, a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
The document describes research into improving the effectiveness of information retrieval systems using an adaptive genetic algorithm. A genetic algorithm with variable crossover and mutation probabilities (adaptive GA) is investigated. The adaptive GA is tested on 242 Arabic abstracts using three information retrieval models: vector space model, extended Boolean model, and language model. Results show the adaptive GA approach improves retrieval effectiveness over traditional genetic algorithms and baseline information retrieval systems, as measured by average recall and precision. Key aspects of the adaptive GA used include variable crossover and mutation probabilities tuned during the search process, and fitness functions based on document retrieval order.
A genetic algorithm approach for predicting ribonucleic acid sequencing data ...TELKOMNIKA JOURNAL
Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.
This document describes two machine learning techniques, particle swarm optimization with support vector machines (PSO-SVM) and recursive feature elimination with support vector machines (RFE-SVM), that were used to classify autism neuroimaging data from the Autism Brain Imaging Data Exchange database. PSO-SVM was used to select discriminative features for classification, while RFE-SVM ranked features by importance. Both techniques aimed to improve classification accuracy and reduce overfitting by selecting optimal feature subsets from the high-dimensional neuroimaging data. The results could help develop brain-based diagnostic criteria for autism.
Genome structure prediction a review over soft computing techniqueseSAT Journals
Abstract There are some techniques like spectrometry or crystallography for the determination of DNA, RNA or protein structures. These processes provide very accurate results for the structure estimation. But these conventional techniques are very slow and could be applied over a few special cases only. Soft computing techniques guarantee a near appropriate results in much smaller time and have very large applicability. These techniques are much easier to apply. Different approaches have been used in soft computing including nature inspired computing for estimation of genome structures with a considerable accuracy of results. This paper provides a review over different soft computing techniques been applied along with application method for the determination of genome structure. Keywords—DNA, RNA, proteins, structure, soft computing, techniques.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...Avishek Choudhury
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
IRJET- Prediction of Heart Disease using RNN AlgorithmIRJET Journal
This document discusses using a recurrent neural network (RNN) algorithm to predict heart disease. It proposes a method called prognosis prediction using RNN (PP-RNN) that uses multiple RNNs to learn from patient diagnosis code sequences in order to predict high-risk diseases. The experimental results show that the proposed PP-RNN method can achieve more accurate results than existing methods for predicting heart disease risk. It also provides background on related works using other techniques like decision trees, clustering, and AdaBoost for heart disease prediction.
This document describes a study that uses machine learning algorithms to efficiently predict DNA-binding proteins. Support vector machines and cascade correlation neural networks are optimized and compared to determine the best performing model. The SVM model achieves 86.7% accuracy at predicting DNA-binding proteins using features like overall charge, patch size, and amino acid composition of proteins. The CCNN model achieves lower accuracy of 75.4%. The study aims to improve on previous work by using the standard jack-knife validation technique to evaluate model performance on unseen data.
Application of Hybrid Genetic Algorithm Using Artificial Neural Network in Da...IOSRjournaljce
The main purpose of data mining is to extract knowledge from large amount of data. Artificial Neural network (ANN) has already been applied in a variety of domains with remarkable success. This paper presents the application of hybrid model for stroke disease that integrates Genetic algorithm and back propagation algorithm. Selecting a good subset of features, without sacrificing accuracy, is of great importance for neural networks to be successfully applied to the area. In addition the hybrid model that leads to further improvised categorization, accuracy compared to the result produced by genetic algorithm alone. In this study, a new hybrid model of Neural Networks and Genetic Algorithm (GA) to initialize and optimize the connection weights of ANN so as to improve the performance of the ANN and the same has been applied in a medical problem of predicting stroke disease for verification of the results.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
The document describes research into improving the effectiveness of information retrieval systems using an adaptive genetic algorithm. A genetic algorithm with variable crossover and mutation probabilities (adaptive GA) is investigated. The adaptive GA is tested on 242 Arabic abstracts using three information retrieval models: vector space model, extended Boolean model, and language model. Results show the adaptive GA approach improves retrieval effectiveness over traditional genetic algorithms and baseline information retrieval systems, as measured by average recall and precision. Key aspects of the adaptive GA used include variable crossover and mutation probabilities tuned during the search process, and fitness functions based on document retrieval order.
A genetic algorithm approach for predicting ribonucleic acid sequencing data ...TELKOMNIKA JOURNAL
Malaria larvae accept explosive variable lifecycle as they spread across numerous mosquito vector stratosphere. Transcriptomes arise in thousands of diverse parasites. Ribonucleic acid sequencing (RNA-seq) is a prevalent gene expression that has led to enhanced understanding of genetic queries. RNA-seq tests transcript of gene expression, and provides methodological enhancements to machine learning procedures. Researchers have proposed several methods in evaluating and learning biological data. Genetic algorithm (GA) as a feature selection process is used in this study to fetch relevant information from the RNA-Seq Mosquito Anopheles gambiae malaria vector dataset, and evaluates the results using kth nearest neighbor (KNN) and decision tree classification algorithms. The experimental results obtained a classification accuracy of 88.3 and 98.3 percents respectively.
This document describes two machine learning techniques, particle swarm optimization with support vector machines (PSO-SVM) and recursive feature elimination with support vector machines (RFE-SVM), that were used to classify autism neuroimaging data from the Autism Brain Imaging Data Exchange database. PSO-SVM was used to select discriminative features for classification, while RFE-SVM ranked features by importance. Both techniques aimed to improve classification accuracy and reduce overfitting by selecting optimal feature subsets from the high-dimensional neuroimaging data. The results could help develop brain-based diagnostic criteria for autism.
Genome structure prediction a review over soft computing techniqueseSAT Journals
Abstract There are some techniques like spectrometry or crystallography for the determination of DNA, RNA or protein structures. These processes provide very accurate results for the structure estimation. But these conventional techniques are very slow and could be applied over a few special cases only. Soft computing techniques guarantee a near appropriate results in much smaller time and have very large applicability. These techniques are much easier to apply. Different approaches have been used in soft computing including nature inspired computing for estimation of genome structures with a considerable accuracy of results. This paper provides a review over different soft computing techniques been applied along with application method for the determination of genome structure. Keywords—DNA, RNA, proteins, structure, soft computing, techniques.
Prognosticating Autism Spectrum Disorder Using Artificial Neural Network: Lev...Avishek Choudhury
Autism spectrum condition (ASC) or autism spectrum disorder (ASD) is primarily identified with the help of behavioral indications encompassing social, sensory and motor characteristics. Although categorized, recurring motor actions are measured during diagnosis, quantifiable measures that ascertain kinematic physiognomies in the movement configurations of autistic persons are not adequately studied, hindering the advances in understanding the etiology of motor mutilation. Subject aspects such as behavioral characters that influences ASD need further exploration. Presently, limited autism datasets concomitant with screening ASD are available, and a majority of them are genetic. Hence, in this study, we used a dataset related to autism screening enveloping ten behavioral and ten personal attributes that have been effective in diagnosing ASD cases from controls in behavior science. ASD diagnosis is time exhaustive and uneconomical. The burgeoning ASD cases worldwide mandate a need for the fast and economical screening tool. Our study aimed to implement an artificial neural network with the Levenberg- Marquardt algorithm to detect ASD and examine its predictive accuracy. Consecutively, develop a clinical decision support system for early ASD identification.
IRJET- Prediction of Heart Disease using RNN AlgorithmIRJET Journal
This document discusses using a recurrent neural network (RNN) algorithm to predict heart disease. It proposes a method called prognosis prediction using RNN (PP-RNN) that uses multiple RNNs to learn from patient diagnosis code sequences in order to predict high-risk diseases. The experimental results show that the proposed PP-RNN method can achieve more accurate results than existing methods for predicting heart disease risk. It also provides background on related works using other techniques like decision trees, clustering, and AdaBoost for heart disease prediction.
This document describes a study that uses machine learning algorithms to efficiently predict DNA-binding proteins. Support vector machines and cascade correlation neural networks are optimized and compared to determine the best performing model. The SVM model achieves 86.7% accuracy at predicting DNA-binding proteins using features like overall charge, patch size, and amino acid composition of proteins. The CCNN model achieves lower accuracy of 75.4%. The study aims to improve on previous work by using the standard jack-knife validation technique to evaluate model performance on unseen data.
This document summarizes a research paper that proposes using a genetic algorithm to efficiently cluster wireless sensor nodes. The genetic algorithm aims to minimize the total communication distance between sensors and the base station in order to prolong the network lifetime. Simulation results showed that the genetic algorithm can quickly find good clustering solutions that reduce energy consumption compared to previous clustering methods. The full paper provides details on wireless sensor networks, related clustering algorithms, genetic algorithms, and the proposed genetic algorithm-based clustering method.
Delineation of techniques to implement on the enhanced proposed model using d...ijdms
In post genomic era with the advent of new technologies a huge amount of complex molecular data are
generated with high throughput. The management of this biological data is definitely a challenging task
due to complexity and heterogeneity of data for discovering new knowledge. Issues like managing noisy
and incomplete data are needed to be dealt with. Use of data mining in biological domain has made its
inventory success. Discovering new knowledge from the biological data is a major challenge in data
mining technique. The novelty of the proposed model is its combined use of intelligent techniques to classify
the protein sequence faster and efficiently. Use of FFT, fuzzy classifier, String weighted algorithm, gram
encoding method, neural network model and rough set classifier in a single model and in an appropriate
place can enhance the quality of the classification system .Thus the primary challenge is to identify and
classify the large protein sequences in a very fast and easy but intellectual way to decrease the time
complexity and space complexity.
Software Defect Prediction Using Radial Basis and Probabilistic Neural NetworksEditor IJCATR
This document discusses using neural networks for software defect prediction. It examines the effectiveness of using a radial basis function neural network and a probabilistic neural network on prediction accuracy and defect prediction compared to other techniques. The key findings are that neural networks provide an acceptable level of accuracy for defect prediction but perform poorly at actual defect prediction. Probabilistic neural networks performed consistently better than other techniques across different datasets in terms of prediction accuracy and defect prediction ability. The document recommends using an ensemble of different software defect prediction models rather than relying on a single technique.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Classification of medical datasets using back propagation neural network powe...IJECEIAES
The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immu...IJECEIAES
This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multi-objective testing functions were then used. The result also illustrated that the modified approach still had the best performance.
A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network DatasetsDrjabez
1. The document proposes a genetic-fuzzy based method for automatic intrusion detection using network datasets. It combines fuzzy set theory with genetic algorithms to extract rules for both discrete and continuous attributes to detect normal and intrusion patterns.
2. The method was tested on KDD99 Cup and DARPA98 network intrusion detection datasets and showed high detection rates with low false alarm rates for both misuse detection and anomaly detection.
3. By extracting many rules to represent normal network behavior patterns, the proposed genetic-fuzzy approach can detect new or unknown intrusions based on anomalies without requiring prior domain expertise on intrusion patterns.
- The document discusses various approaches for applying machine learning and artificial intelligence to drug discovery.
- It describes how molecules and proteins can be represented as graphs, fingerprints, or sequences to be used as input for models.
- Different tasks in drug discovery like target binding prediction, generative design of new molecules, and drug repurposing are framed as questions that AI models can aim to answer.
- Techniques discussed include graph neural networks, reinforcement learning, and conditional generation using techniques like translation models.
- Several recent works applying these approaches for tasks like predicting drug-target interactions and generating synthesizable molecules are referenced.
Network embedding in biomedical data scienceArindam Ghosh
Excerpts from the paper:
What is it?
Network embedding aims at converting the network into a low-dimensional space while structural information of the network is preserved.
In this way, nodes and/or edges of the network can be represented as compacted yet informative vectors in the embedding space.
Advantages:
Typical non-network-based machine learning methods such as linear regression, Support Vector Machine (SVM) and decision forest, which have been demonstrated to be effective and efficient as the state-of-the-art techniques, can be applied to such vectors.
Current status:
Efforts of applying network embedding to improve biomedical data analysis are already planned or underway.
Difficulties:
The biomedical networks are sparse, noisy, incomplete, heterogeneous and usually consist of biomedical text and other domain knowledge. It makes embedding tasks more complicated than other application fields.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
This document summarizes a research paper that analyzes machine learning algorithms for intrusion detection using the UNSW-NB15 dataset. It compares the performance of classifiers like KNN, SGD, Random Forest, Logistic Regression, and Naive Bayes, both with and without feature selection. Chi-Square feature selection is applied to reduce irrelevant features before training the classifiers. The classifiers' performance is evaluated based on metrics like accuracy, precision, recall, F1-score, true positive rate and false positive rate. The paper finds that feature selection can improve classifiers' performance for intrusion detection.
Solar Irradiation Prediction using back Propagation and Artificial Neural Net...ijtsrd
The document discusses using artificial neural networks to predict solar irradiation. It proposes a model using ANN with the Levenberg-Marquardt algorithm for backpropagation. The model aims to more accurately estimate available solar power by forecasting fluctuating solar irradiation levels. It achieves high accuracy of 97.74% and low error rate of 2.76% according to mean absolute percentage error and regression analysis. This performance improvement over contemporary techniques demonstrates ANN's effectiveness for nonlinear solar irradiation forecasting.
Inference of Nonlinear Gene Regulatory Networks through Optimized Ensemble of...Arinze Akutekwe
Comprehensive understanding of gene regulatory
networks (GRNs) is a major challenge in systems biology. Most
methods for modeling and inferring the dynamics of GRNs,
such as those based on state space models, vector autoregressive
models and G1DBN algorithm, assume linear dependencies
among genes. However, this strong assumption does not make
for true representation of time-course relationships across the
genes, which are inherently nonlinear. Nonlinear modeling
methods such as the S-systems and causal structure
identification (CSI) have been proposed, but are known to be
statistically inefficient and analytically intractable in high
dimensions. To overcome these limitations, we propose an
optimized ensemble approach based on support vector
regression (SVR) and dynamic Bayesian networks (DBNs). The
method called SVR-DBN, uses nonlinear kernels of the SVR to
infer the temporal relationships among genes within the DBN
framework. The two-stage ensemble is further improved by
SVR parameter optimization using Particle Swarm
Optimization. Results on eight insilico-generated datasets, and
two real world datasets of Drosophila Melanogaster and
Escherichia Coli, show that our method outperformed the
G1DBN algorithm by a total average accuracy of 12%. We
further applied our method to model the time-course
relationships of ovarian carcinoma. From our results, four hub
genes were discovered. Stratified analysis further showed that
the expression levels Prostrate differentiation factor and BTG
family member 2 genes, were significantly increased by the
cisplatin and oxaliplatin platinum drugs; while expression levels
of Polo-like kinase and Cyclin B1 genes, were both decreased by
the platinum drugs. These hub genes might be potential
biomarkers for ovarian carcinoma.
IRJET- Plant Disease Detection and Classification using Image Processing a...IRJET Journal
This document describes a method for detecting and classifying plant diseases using image processing and artificial neural networks. The method involves preprocessing images through grayscaling, resizing and filtering. K-means clustering is used to segment infected leaf regions. Features are extracted from segmented images and fed into feedforward and cascaded feedforward neural networks for disease classification. The method achieved accurate classification of several common plant diseases with fewer iterations and better performance than traditional feedforward backpropagation neural networks. This automatic disease detection approach could help improve agricultural productivity by facilitating early detection on large farms.
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SURVEY ON MODELLING METHODS APPLICABLE TO GENE REGULATORY NETWORKijbbjournal
Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives
information about at which different environmental conditions genes of particular interest get over
expressed or under expressed. Modelling of GRN is nothing but finding interactive relationships between
genes. Interaction can be positive or negative. For inference of GRN, time series data provided by
Microarray technology is used. Key factors to be considered while constructing GRN are scalability,
robustness, reliability and maximum detection of true positive interactions between genes. This paper
gives detailed technical review of existing methods applied for building of GRN along with scope for
future work.
The National Resource for Network Biology (NRNB) held its External Advisory Council meeting on December 12, 2012. The NRNB is focused on developing network biology tools and collaborating with investigators. It oversees various technology research and development projects, software releases including Cytoscape 3.0, collaboration projects, and outreach/training events. The meeting agenda covered progress updates and sought advice on future plans.
An intrusion detection system for packet and flow based networks using deep n...IJECEIAES
Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets. The advent of big data has agitated the comprehensive assistance in cyber security by forwarding a brunch of affluent algorithms to classify and analysis patterns and making a better prediction more efficiently. In this study, to detect intrusion a detection model has been propounded applying deep neural networks. We applied the suggested model on the latest dataset available at online, formatted with packet based, flow based data and some additional metadata. The dataset is labeled and imbalanced with 79 attributes and some classes having much less training samples compared to other classes. The proposed model is build using Keras and Google Tensorflow deep learning environment. Experimental result shows that intrusions are detected with the accuracy over 99% for both binary and multiclass classification with selected best features. Receiver operating characteristics (ROC) and precision-recall curve average score is also 1. The outcome implies that Deep Neural Networks offers a novel research model with great accuracy for intrusion detection model, better than some models presented in the literature.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
This document summarizes a research paper that proposes using a genetic algorithm to efficiently cluster wireless sensor nodes. The genetic algorithm aims to minimize the total communication distance between sensors and the base station in order to prolong the network lifetime. Simulation results showed that the genetic algorithm can quickly find good clustering solutions that reduce energy consumption compared to previous clustering methods. The full paper provides details on wireless sensor networks, related clustering algorithms, genetic algorithms, and the proposed genetic algorithm-based clustering method.
Delineation of techniques to implement on the enhanced proposed model using d...ijdms
In post genomic era with the advent of new technologies a huge amount of complex molecular data are
generated with high throughput. The management of this biological data is definitely a challenging task
due to complexity and heterogeneity of data for discovering new knowledge. Issues like managing noisy
and incomplete data are needed to be dealt with. Use of data mining in biological domain has made its
inventory success. Discovering new knowledge from the biological data is a major challenge in data
mining technique. The novelty of the proposed model is its combined use of intelligent techniques to classify
the protein sequence faster and efficiently. Use of FFT, fuzzy classifier, String weighted algorithm, gram
encoding method, neural network model and rough set classifier in a single model and in an appropriate
place can enhance the quality of the classification system .Thus the primary challenge is to identify and
classify the large protein sequences in a very fast and easy but intellectual way to decrease the time
complexity and space complexity.
Software Defect Prediction Using Radial Basis and Probabilistic Neural NetworksEditor IJCATR
This document discusses using neural networks for software defect prediction. It examines the effectiveness of using a radial basis function neural network and a probabilistic neural network on prediction accuracy and defect prediction compared to other techniques. The key findings are that neural networks provide an acceptable level of accuracy for defect prediction but perform poorly at actual defect prediction. Probabilistic neural networks performed consistently better than other techniques across different datasets in terms of prediction accuracy and defect prediction ability. The document recommends using an ensemble of different software defect prediction models rather than relying on a single technique.
AN EFFICIENT PSO BASED ENSEMBLE CLASSIFICATION MODEL ON HIGH DIMENSIONAL DATA...ijsc
As the size of the biomedical databases are growing day by day, finding an essential features in the disease prediction have become more complex due to high dimensionality and sparsity problems. Also, due to the
availability of a large number of micro-array datasets in the biomedical repositories, it is difficult to analyze, predict and interpret the feature information using the traditional feature selection based classification models. Most of the traditional feature selection based classification algorithms have computational issues such as dimension reduction, uncertainty and class imbalance on microarray datasets. Ensemble classifier is one of the scalable models for extreme learning machine due to its high efficiency, the fast processing speed for real-time applications. The main objective of the feature selection
based ensemble learning models is to classify the high dimensional data with high computational efficiency
and high true positive rate on high dimensional datasets. In this proposed model an optimized Particle swarm optimization (PSO) based Ensemble classification model was developed on high dimensional microarray
datasets. Experimental results proved that the proposed model has high computational efficiency compared to the traditional feature selection based classification models in terms of accuracy , true positive rate and error rate are concerned.
Classification of medical datasets using back propagation neural network powe...IJECEIAES
The classification is a one of the most indispensable domains in the data mining and machine learning. The classification process has a good reputation in the area of diseases diagnosis by computer systems where the progress in smart technologies of computer can be invested in diagnosing various diseases based on data of real patients documented in databases. The paper introduced a methodology for diagnosing a set of diseases including two types of cancer (breast cancer and lung), two datasets for diabetes and heart attack. Back Propagation Neural Network plays the role of classifier. The performance of neural net is enhanced by using the genetic algorithm which provides the classifier with the optimal features to raise the classification rate to the highest possible. The system showed high efficiency in dealing with databases differs from each other in size, number of features and nature of the data and this is what the results illustrated, where the ratio of the classification reached to 100% in most datasets).
The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immu...IJECEIAES
This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multi-objective testing functions were then used. The result also illustrated that the modified approach still had the best performance.
A Study on Genetic-Fuzzy Based Automatic Intrusion Detection on Network DatasetsDrjabez
1. The document proposes a genetic-fuzzy based method for automatic intrusion detection using network datasets. It combines fuzzy set theory with genetic algorithms to extract rules for both discrete and continuous attributes to detect normal and intrusion patterns.
2. The method was tested on KDD99 Cup and DARPA98 network intrusion detection datasets and showed high detection rates with low false alarm rates for both misuse detection and anomaly detection.
3. By extracting many rules to represent normal network behavior patterns, the proposed genetic-fuzzy approach can detect new or unknown intrusions based on anomalies without requiring prior domain expertise on intrusion patterns.
- The document discusses various approaches for applying machine learning and artificial intelligence to drug discovery.
- It describes how molecules and proteins can be represented as graphs, fingerprints, or sequences to be used as input for models.
- Different tasks in drug discovery like target binding prediction, generative design of new molecules, and drug repurposing are framed as questions that AI models can aim to answer.
- Techniques discussed include graph neural networks, reinforcement learning, and conditional generation using techniques like translation models.
- Several recent works applying these approaches for tasks like predicting drug-target interactions and generating synthesizable molecules are referenced.
Network embedding in biomedical data scienceArindam Ghosh
Excerpts from the paper:
What is it?
Network embedding aims at converting the network into a low-dimensional space while structural information of the network is preserved.
In this way, nodes and/or edges of the network can be represented as compacted yet informative vectors in the embedding space.
Advantages:
Typical non-network-based machine learning methods such as linear regression, Support Vector Machine (SVM) and decision forest, which have been demonstrated to be effective and efficient as the state-of-the-art techniques, can be applied to such vectors.
Current status:
Efforts of applying network embedding to improve biomedical data analysis are already planned or underway.
Difficulties:
The biomedical networks are sparse, noisy, incomplete, heterogeneous and usually consist of biomedical text and other domain knowledge. It makes embedding tasks more complicated than other application fields.
Drug discovery and development is a long and expensive process and over time has notoriously bucked Moore’s law that it now has its own law called Eroom’s Law named after it (the opposite of Moore’s). It is estimated that the attrition rate of drug candidates is up to 96% and the average cost to develop a new drug has reached almost $2.5 billion in recent years. One of the major causes for the high attrition rate is drug safety, which accounts for 30% of the failures.
Even if a drug is approved in market, it could be withdrawn due to safety problems. Therefore, evaluating drug safety extensively as early as possible is paramount in accelerating drug discovery and development. This talk provides a high-level overview of the current process of rational drug design that has been in place for many decades and covers some of the major areas where the application of AI, Deep learning and ML based techniques have had the most gains.
Specifically, this talk covers a variety of drug safety related AI and ML based techniques currently in use which can generally divided into 3 main categories:
1. Discovery,
2. Toxicity and Safety, and
3. Post-Market Monitoring.
We will address the recent progress in predictive models and techniques built for various toxicities. It will also cover some publicly available databases, tools and platforms available to easily leverage them.
We will also compare and contrast various modeling techniques including deep learning techniques and their accuracy using recent research. Finally, the talk will address some of the remaining challenges and limitations yet to be addressed in the area of drug discovery and safety assessment.
ANALYSIS OF MACHINE LEARNING ALGORITHMS WITH FEATURE SELECTION FOR INTRUSION ...IJNSA Journal
This document summarizes a research paper that analyzes machine learning algorithms for intrusion detection using the UNSW-NB15 dataset. It compares the performance of classifiers like KNN, SGD, Random Forest, Logistic Regression, and Naive Bayes, both with and without feature selection. Chi-Square feature selection is applied to reduce irrelevant features before training the classifiers. The classifiers' performance is evaluated based on metrics like accuracy, precision, recall, F1-score, true positive rate and false positive rate. The paper finds that feature selection can improve classifiers' performance for intrusion detection.
Solar Irradiation Prediction using back Propagation and Artificial Neural Net...ijtsrd
The document discusses using artificial neural networks to predict solar irradiation. It proposes a model using ANN with the Levenberg-Marquardt algorithm for backpropagation. The model aims to more accurately estimate available solar power by forecasting fluctuating solar irradiation levels. It achieves high accuracy of 97.74% and low error rate of 2.76% according to mean absolute percentage error and regression analysis. This performance improvement over contemporary techniques demonstrates ANN's effectiveness for nonlinear solar irradiation forecasting.
Inference of Nonlinear Gene Regulatory Networks through Optimized Ensemble of...Arinze Akutekwe
Comprehensive understanding of gene regulatory
networks (GRNs) is a major challenge in systems biology. Most
methods for modeling and inferring the dynamics of GRNs,
such as those based on state space models, vector autoregressive
models and G1DBN algorithm, assume linear dependencies
among genes. However, this strong assumption does not make
for true representation of time-course relationships across the
genes, which are inherently nonlinear. Nonlinear modeling
methods such as the S-systems and causal structure
identification (CSI) have been proposed, but are known to be
statistically inefficient and analytically intractable in high
dimensions. To overcome these limitations, we propose an
optimized ensemble approach based on support vector
regression (SVR) and dynamic Bayesian networks (DBNs). The
method called SVR-DBN, uses nonlinear kernels of the SVR to
infer the temporal relationships among genes within the DBN
framework. The two-stage ensemble is further improved by
SVR parameter optimization using Particle Swarm
Optimization. Results on eight insilico-generated datasets, and
two real world datasets of Drosophila Melanogaster and
Escherichia Coli, show that our method outperformed the
G1DBN algorithm by a total average accuracy of 12%. We
further applied our method to model the time-course
relationships of ovarian carcinoma. From our results, four hub
genes were discovered. Stratified analysis further showed that
the expression levels Prostrate differentiation factor and BTG
family member 2 genes, were significantly increased by the
cisplatin and oxaliplatin platinum drugs; while expression levels
of Polo-like kinase and Cyclin B1 genes, were both decreased by
the platinum drugs. These hub genes might be potential
biomarkers for ovarian carcinoma.
IRJET- Plant Disease Detection and Classification using Image Processing a...IRJET Journal
This document describes a method for detecting and classifying plant diseases using image processing and artificial neural networks. The method involves preprocessing images through grayscaling, resizing and filtering. K-means clustering is used to segment infected leaf regions. Features are extracted from segmented images and fed into feedforward and cascaded feedforward neural networks for disease classification. The method achieved accurate classification of several common plant diseases with fewer iterations and better performance than traditional feedforward backpropagation neural networks. This automatic disease detection approach could help improve agricultural productivity by facilitating early detection on large farms.
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
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SURVEY ON MODELLING METHODS APPLICABLE TO GENE REGULATORY NETWORKijbbjournal
Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives
information about at which different environmental conditions genes of particular interest get over
expressed or under expressed. Modelling of GRN is nothing but finding interactive relationships between
genes. Interaction can be positive or negative. For inference of GRN, time series data provided by
Microarray technology is used. Key factors to be considered while constructing GRN are scalability,
robustness, reliability and maximum detection of true positive interactions between genes. This paper
gives detailed technical review of existing methods applied for building of GRN along with scope for
future work.
The National Resource for Network Biology (NRNB) held its External Advisory Council meeting on December 12, 2012. The NRNB is focused on developing network biology tools and collaborating with investigators. It oversees various technology research and development projects, software releases including Cytoscape 3.0, collaboration projects, and outreach/training events. The meeting agenda covered progress updates and sought advice on future plans.
An intrusion detection system for packet and flow based networks using deep n...IJECEIAES
Study on deep neural networks and big data is merging now by several aspects to enhance the capabilities of intrusion detection system (IDS). Many IDS models has been introduced to provide security over big data. This study focuses on the intrusion detection in computer networks using big datasets. The advent of big data has agitated the comprehensive assistance in cyber security by forwarding a brunch of affluent algorithms to classify and analysis patterns and making a better prediction more efficiently. In this study, to detect intrusion a detection model has been propounded applying deep neural networks. We applied the suggested model on the latest dataset available at online, formatted with packet based, flow based data and some additional metadata. The dataset is labeled and imbalanced with 79 attributes and some classes having much less training samples compared to other classes. The proposed model is build using Keras and Google Tensorflow deep learning environment. Experimental result shows that intrusions are detected with the accuracy over 99% for both binary and multiclass classification with selected best features. Receiver operating characteristics (ROC) and precision-recall curve average score is also 1. The outcome implies that Deep Neural Networks offers a novel research model with great accuracy for intrusion detection model, better than some models presented in the literature.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Influence of alkaline substances (carbonates and bicarbonates of sodium) in w...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Study and comparison of various communication based protective relaying schem...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Performance analysis of cmos comparator and cntfet comparator designeSAT Publishing House
This document summarizes a study comparing the performance of CNTFET and CMOS comparator designs. CNTFET comparators were simulated using CADENCE showing faster propagation delays, lower power consumption, and improved transient response compared to CMOS comparators. Specifically, the CNTFET comparator had a rise time of 142.1ns versus 1.03ns for CMOS, and fall time of 164.18ns versus 821.476ns for CMOS. Average power was also lower at 118mW for CNTFET versus 910mW for CMOS. Due to these advantages, CNTFETs may replace silicon transistors as the performance of silicon MOSFETs reaches scaling limitations.
This document summarizes a study on evaluating the performance of intake tower dams in India during recent earthquakes. Intake towers are tall, hollow reinforced concrete structures that form the entrance to reservoir outlet works. The study aims to understand how parameters like depth of submergence, wall thickness, and slenderness ratio affect the dynamic behavior of intake towers during earthquakes. A simplified circular cylindrical tower model was analyzed in SAP2000 considering hydrodynamic added mass from surrounding and inside water. Dynamic analyses including modal and time history analyses were performed for different soil conditions and earthquakes in India from 2005-2013. The results show the effect of these parameters and soil conditions on the displacement and acceleration response of the intake towers.
The document summarizes a finite element analysis of a torpedo battery tray conducted to evaluate its performance under severe vibration. The battery tray was modeled and meshed in ANSYS. Static, modal, harmonic and shock analyses were performed by applying loads in different axes. Results from the ANSYS simulation like deformation, stresses, natural frequencies and frequency response were compared to experimental test data. The maximum errors between simulation and experimental results for deformation and stresses were within 10%.
A novel hybrid communication technique involving power line communication and...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Static analysis of c s short cylindrical shell under internal liquid pressure...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
A heuristic approach for optimizing travel planning using genetics algorithmeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Accuracy enhancement of srtm and aster dems using weight estimation regressio...eSAT Publishing House
This document assesses the accuracy of SRTM and ASTER DEMs in Egypt by comparing DEM elevations to GPS ground control points (GCPs) in two study areas with different topography: a flat delta region and a hilly desert region. Root mean square errors (RMSEs) for SRTM ranged from 15.6m in the delta to 7.9m in the desert, and for ASTER ranged from 13.2m in the delta to 12.4m in the desert. A new approach using weight estimation regression models with topographic indices and aspects as predictors improved accuracy, reducing standard errors of estimates.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
IRJET- Gene Mutation Data using Multiplicative Adaptive Algorithm and Gene On...IRJET Journal
This document presents a methodology for analyzing gene mutation data using ontologies and association rule mining. It aims to develop a common knowledge base for genomic and proteomic analysis by integrating multiple data sources. The methodology involves using k-nearest neighbors algorithm to find similar genes, an iterative multiplicative updating algorithm to solve optimization problems, and SNCoNMF to identify co-regulatory modules between genes, microRNAs and transcription factors. The results are represented using a Bayesian rose tree for efficient visualization of associations between genetic components and diseases.
Clustering Approaches for Evaluation and Analysis on Formal Gene Expression C...rahulmonikasharma
Enormous generation of biological data and the need of analysis of that data led to the generation of the field Bioinformatics. Data mining is the stream which is used to derive, analyze the data by exploring the hidden patterns of the biological data. Though, data mining can be used in analyzing biological data such as genomic data, proteomic data here Gene Expression (GE) Data is considered for evaluation. GE is generated from Microarrays such as DNA and oligo micro arrays. The generated data is analyzed through the clustering techniques of data mining. This study deals with an implement the basic clustering approach K-Means and various clustering approaches like Hierarchal, Som, Click and basic fuzzy based clustering approach. Eventually, the comparative study of those approaches which lead to the effective approach of cluster analysis of GE.The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes less clustering time when compared with existing algorithms.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Comparative analysis of dynamic programming algorithms to find similarity in ...eSAT Journals
Abstract There exist many computational methods for finding similarity in gene sequence, finding suitable methods that gives optimal similarity is difficult task. Objective of this project is to find an appropriate method to compute similarity in gene/protein sequence, both within the families and across the families. Many dynamic programming algorithms like Levenshtein edit distance; Longest Common Subsequence and Smith-waterman have used dynamic programming approach to find similarities between two sequences. But none of the method mentioned above have used real benchmark data sets. They have only used dynamic programming algorithms for synthetic data. We proposed a new method to compute similarity. The performance of the proposed algorithm is evaluated using number of data sets from various families, and similarity value is calculated both within the family and across the families. A comparative analysis and time complexity of the proposed method reveal that Smith-waterman approach is appropriate method when gene/protein sequence belongs to same family and Longest Common Subsequence is best suited when sequence belong to two different families. Keywords - Bioinformatics, Gene, Gene Sequencing, Edit distance, String Similarity.
CCC-Bicluster Analysis for Time Series Gene Expression DataIRJET Journal
The document presents a CCC-Biclustering (Contiguous Column Coherence) algorithm for identifying biclusters in time series gene expression data. The algorithm finds maximal biclusters with adjacent/contiguous columns in linear time using Ukkonen's suffix tree construction algorithm and discretized gene expression matrices. The algorithm was applied to a Saccharomyces cerevisiae gene expression time series in response to heat stress. It identifies coherent expression patterns shared among genes over contiguous time points, potentially revealing relevant regulatory modules.
Pattern recognition system based on support vector machinesAlexander Decker
This document describes a study that uses support vector machines (SVM) to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-HIV activity of 1,3,4-oxadiazole substituted naphthyridine derivatives based on their molecular descriptors. The SVM model achieved a cross-validation R2 value of 0.90 and RMSE of 0.145, outperforming artificial neural network and multiple linear regression models. An external validation on an independent test set found the SVM model had an R value of 0.96 and RMSE of 0.166, demonstrating good predictive ability.
بعض (وليس الكل) ملخصات الأبحاث الجيدة المنشورة فى بعض المجلات الجيدة وفيها تنوع من الافكار الابحاث الابتكارية التى يخدم فيها علوم الحاسبات فيها - انها تطبيقات حياتية
Gene Selection for Sample Classification in Microarray: Clustering Based MethodIOSR Journals
This document describes a clustering-based method for gene selection to classify samples in microarray data. It involves calculating the relevance of each gene to class labels and the redundancy between genes using mutual information. Genes are clustered based on their relevance, with the most relevant gene selected as the cluster representative. Min-hash clustering is then applied to reduce redundant genes and cluster size. The goal is to select a minimal set of non-redundant genes that can accurately classify samples by reducing noise from irrelevant genes.
APPLICATION OF CLONAL SELECTION IMMUNE SYSTEM METHOD FOR OPTIMIZATION OF DIST...UniversitasGadjahMada
This paper proposes an application of clonal selection immune system method for optimization of distribution network. The distribution network with high-performance is a network that has a low power loss, better voltage profile, and loading balance among feeders. The task for improving the performance of the distribution network is optimization of network configuration. The optimization has become a necessary study with the presence of DG in entire networks. In this work, optimization of network configuration is based on an AIS algorithm. The methodology has been tested in a model of 33 bus IEEE radial distribution networks with and without DG integration. The results have been showed that the optimal configuration of the distribution network is able to reduce power loss and to improve the voltage profile of the distribution network significantly.
Performance analysis of neural network models for oxazolines and oxazoles der...ijistjournal
Neural networks have been used successfully to a br
oad range of areas such as business, data mining, d
rug
discovery and biology. In medicine, neural network
s have been applied widely in medical diagnosis,
detection and evaluation of new drugs and treatment
cost estimation. In addition, neural networks have
begin practice in data mining strategies for the a
im of prediction, knowledge discovery. This paper
will
present the application of neural networks for the
prediction and analysis of antitubercular activity
of
Oxazolines and Oxazoles derivatives. This study pre
sents techniques based on the development of Single
hidden layer neural network (SHLFFNN), Gradient Des
cent Back propagation neural network (GDBPNN),
Gradient Descent Back propagation with momentum neu
ral network (GDBPMNN), Back propagation with
Weight decay neural network (BPWDNN) and Quantile r
egression neural network (QRNN) of artificial
neural network (ANN) models Here, we comparatively
evaluate the performance of five neural network
techniques. The evaluation of the efficiency of eac
h model by ways of benchmark experiments is an
accepted application. Cross-validation and resampli
ng techniques are commonly used to derive point
estimates of the performances which are compared to
identify methods with good properties. Predictiv
e
accuracy was evaluated using the root mean squared
error (RMSE), Coefficient determination(
), mean
absolute error(MAE), mean percentage error(MPE) and
relative square error(RSE). We found that all five
neural network models were able to produce feasible
models. QRNN model is outperforms with all
statistical tests amongst other four models.
Peter Langfelder presented on weighted gene co-expression network analysis of HD data. Key points:
- WGCNA identified gene modules in mouse striatum associated with CAG repeat length. Neuronal modules were down with increasing repeats while oligodendrocyte modules were up.
- Human HD brain regions showed common and region-specific responses. A neuronal module was down across all regions while astrocyte and microglial modules were up.
- Consensus modules identified co-expressed genes consistently changed across multiple human HD datasets, providing robust modules for further investigation.
Applications of Artificial Neural Networks in Cancer PredictionIRJET Journal
This document discusses applications of artificial neural networks in cancer prediction and prognosis. It summarizes several studies that have used ANNs to predict breast cancer prognosis and recurrence, as well as classify types of lung cancer.
For breast cancer prognosis, a Maximum Entropy Estimation model was shown to outperform multi-layer perceptrons and probabilistic neural networks. For predicting breast cancer recurrence, an ANN achieved the best performance compared to other machine learning algorithms based on accuracy and AUC.
An ANN combined with a genetic algorithm was also able to successfully identify genes that classify lung cancer status. The ANN-GA model achieved over 97% accuracy in classifying different types of lung cancer based on gene expression data.
This document describes research on using Bayesian networks to model gene expression data related to breast cancer. The goals are to identify new or known gene interactions, examine network properties, and find significant genes. The methodology involves learning networks from 82 genes using different variable types and sample groups. Centrality metrics are used to identify important "hub" genes. Networks are analyzed to determine if they exhibit small-world or scale-free properties common in biological networks. The results could confirm known pathways or identify new ones relevant to breast cancer.
An Efficient PSO Based Ensemble Classification Model on High Dimensional Data...ijsc
This summary provides the high-level information from the document in 3 sentences:
The document proposes a Particle Swarm Optimization (PSO) based ensemble classification model to improve classification of high-dimensional biomedical datasets. It develops an optimized PSO technique to select optimal features and initialize weights for base classifiers in the ensemble model. Experimental results on microarray datasets show the proposed model achieves higher accuracy, true positive rate, and lower error rate compared to traditional feature selection based classification models.
Comparative study of artificial neural network based classification for liver...Alexander Decker
This document presents a comparative study of different artificial neural network (ANN) classification models for predicting liver disease in patients. It evaluates ANN models like backpropagation, radial basis function, self-organizing map, and support vector machine on liver patient data. The support vector machine model achieved the highest accuracy at 99.76% for men data and 97.7% for women data, indicating it may be effective as a predictive tool for liver patients.
PREDICTION OF MALIGNANCY IN SUSPECTED THYROID TUMOUR PATIENTS BY THREE DIFFER...cscpconf
This document compares three classification methods - artificial neural networks, decision trees, and logistic regression - for predicting malignancy in thyroid tumor patients using a clinical dataset. It describes each method and applies them to a dataset of 259 thyroid tumor patients. The artificial neural network achieved 98% accuracy on the training set and 92% on the validation set. The decision tree method used 150 cases to build a model and achieved 86% accuracy. Logistic regression analysis resulted in 88% accuracy. The artificial neural network was able to accurately predict malignancy and identified important attributes like multiple nodules and family cancer history.
Single parent mating in genetic algorithm for real robotic system identificationIAESIJAI
System identification (SI) is a method of determining a mathematical model
for a system given a set of input-output data. A representation is made using
a mathematical model based on certain specified assumptions. In SI, model
structure selection is a step where a model structure perceived as an adequate
system representation is selected. A typical rule is that the final model must
have a good balance between parsimony and accuracy. As a popular search
method, genetic algorithm (GA) is used for selecting a model structure.
However, the optimality of the final model depends much on the
effectiveness of GA operators. This paper presents a mating technique
named single parent mating (SPM) in GA for use in a real robotic SI. This
technique is based on the chromosome structure of the parents such that a
single parent is sufficient in achieving mating that eases the search for the
optimal model. The results show that using three different objective
functions (Akaike information criterion, Bayesian information criterion and
parameter magnitude–based information criterion 2) respectively, GA with
the mating technique is able to find more optimal models than without the
mating technique. Validations show that the selected models using the
mating technique are acceptable.
Optimized Parameter of Wavelet Neural Network (WNN) using INGArahulmonikasharma
Genetic algorithm has been one of the most popular methods for many challenging optimization problems. It is a critical problem in which the evacuation time is an important issues. The continuous air traffic growth and limits of resources, there is a need for reducing the congestion of the airspace system. The main objective of this work is to automatically adapt the airspace configurations, according to the evolution of traffic Niche genetic algorithm(INGA) was used in reliability optimization of software system. And also the searching performance of the genetic algorithm was improved by the stochastic tournament model. The multi-module complex software system reliability allocation effectively. Genetic algorithm (GA) and FGA are compared though seven benchmark function. It can be applied to a wider range of problem including multi-level problem. The uniform schema crossover operator and the non-uniform mutation in the genetic algorithm.
Optimized Parameter of Wavelet Neural Network (WNN) using INGArahulmonikasharma
Genetic algorithm has been one of the most popular methods for many challenging optimization problems. It is a critical problem in which the evacuation time is an important issues. The continuous air traffic growth and limits of resources, there is a need for reducing the congestion of the airspace system. The main objective of this work is to automatically adapt the airspace configurations, according to the evolution of traffic Niche genetic algorithm(INGA) was used in reliability optimization of software system. And also the searching performance of the genetic algorithm was improved by the stochastic tournament model. The multi-module complex software system reliability allocation effectively. Genetic algorithm (GA) and FGA are compared though seven benchmark function. It can be applied to a wider range of problem including multi-level problem. The uniform schema crossover operator and the non-uniform mutation in the genetic algorithm.
Similar to A clonal based algorithm for the reconstruction of (20)
Hudhud cyclone caused extensive damage in Visakhapatnam, India in October 2014, especially to tree cover. This will likely impact the local environment in several ways: increased air pollution as trees absorb less; higher temperatures without tree canopy; increased erosion and landslides. It also created large amounts of waste from destroyed trees. Proper management of solid waste is needed to prevent disease spread. Suggested measures include restoring damaged plants, building fountains to reduce heat, mandating light-colored buildings, improving waste management, and educating public on health risks. Overall, changes are needed to water, land, and waste practices to rebuild the environment after the cyclone removed green cover.
Impact of flood disaster in a drought prone area – case study of alampur vill...eSAT Publishing House
1) In September-October 2009, unprecedented heavy rainfall and dam releases caused widespread flooding in Alampur village in Mahabub Nagar district, a historically drought-prone area.
2) The flood damaged or destroyed homes, buildings, infrastructure, crops, and documents. It displaced many residents and cut off the village.
3) The socioeconomic conditions and mud-based construction of homes in the village exacerbated the flood's impacts, making damage more severe and recovery more difficult.
The document summarizes the Hudhud cyclone that struck Visakhapatnam, India in October 2014. It describes the cyclone's formation, rapid intensification to winds of 175 km/h, and landfall near Visakhapatnam. The cyclone caused extensive damage estimated at over $1 billion and at least 109 deaths in India and Nepal. Infrastructure like buildings, bridges, and power lines were destroyed. Crops and fishing boats were also damaged. The document then discusses coping strategies and improvements needed to disaster management plans to better prepare for future cyclones.
Groundwater investigation using geophysical methods a case study of pydibhim...eSAT Publishing House
This document summarizes the results of a geophysical investigation using vertical electrical sounding (VES) methods at 13 locations around an industrial area in India. The VES data was interpreted to generate geo-electric sections and pseudo-sections showing subsurface resistivity variations. Three main layers were typically identified - a high resistivity topsoil, a weathered middle layer, and a basement rock. Pseudo-sections revealed relatively more weathered areas in the northwest and southwest. Resistivity sections helped identify zones of possible high groundwater potential based on low resistivity anomalies sandwiched between more resistive layers. The study concluded the electrical resistivity method was useful for understanding subsurface geology and identifying areas prospective for groundwater exploration.
Flood related disasters concerned to urban flooding in bangalore, indiaeSAT Publishing House
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A clonal based algorithm for the reconstruction of
1. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 08 | Aug-2013, Available @ http://www.ijret.org 44
A CLONAL BASED ALGORITHM FOR THE RECONSTRUCTION OF
GENETIC NETWORK USING S-SYSTEM
Jereesh A S 1
, Govindan V K 2
1
Research scholar, 2
Professor, Department of Computer Science & Engineering, National Institute of Technology,
Calicut, Kerala, India, jereesh.a.s@gmail.com, vkg@nitc.ac.in
Abstract
Motivation: Gene regulatory network is the network based approach to represent the interactions between genes. DNA microarray is
the most widely used technology for extracting the relationships between thousands of genes simultaneously. Gene microarray
experiment provides the gene expression data for a particular condition and varying time periods. The expression of a particular gene
depends upon the biological conditions and other genes. In this paper, we propose a new method for the analysis of microarray data.
The proposed method makes use of S-system, which is a well-accepted model for the gene regulatory network reconstruction. Since
the problem has multiple solutions, we have to identify an optimized solution. Evolutionary algorithms have been used to solve such
problems. Though there are a number of attempts already been carried out by various researchers, the solutions are still not that
satisfactory with respect to the time taken and the degree of accuracy achieved. Therefore, there is a need of huge amount further
work in this topic for achieving solutions with improved performances.
Results: In this work, we have proposed Clonal selection algorithm for identifying optimal gene regulatory network. The approach is
tested on the real life data: SOS Ecoli DNA repairing gene expression data. It is observed that the proposed algorithm converges
much faster and provides better results than the existing algorithms.
Index Terms: Microarray analysis, Evolutionary Algorithm, Artificial Immune System, S-system, Gene Regulatory
Network, SOS Ecoli DNA repairing, Clonal Selection Algorithm.
-----------------------------------------------------------------------***-----------------------------------------------------------------------
1. INTRODUCTION
DNA microarray is a modern technology, which is used to
analyze the interactions between thousands of genes in parallel
[7]. Exploiting the hybridization property of CDNA, the
transcript abundance information is measured in microarray
experiment. Microarrays have numerous applications. A
particular set of genes are activated for a particular condition.
Identification of activated genes will be useful for recovering
or activating the conditions artificially. Even though the
technology is well developed, direct biological methods
available for finding gene expression are complex. Analysis of
protein expression data is very expensive due to the complex
structures of proteins.
Microarray data analysis involves methodologies and
techniques to analyze the data obtained after the microarray
experiments. The major part of the microarray data analysis is
the numerical analysis of normalized data matrix. Gene
expression analysis is a large-scale experiment, which comes
under functional genomics. Functional genomics deals with
the analysis of large data sets to identify the functions and
interactions between genes [24]. A set of algorithms and
methods are defined for the analysis of microarray data. There
is a tradeoff between the time and accuracy for using an
algorithm for analyzing the microarray data.
Gene Regulatory Network (GRN) is a network of set of genes,
which are involved, in a particular process. In GRN, each node
represents gene and links between genes define the
relationships between those genes. Gene regulatory network is
the network based approach to represent the interactions
between genes. The expression of a particular gene depends
upon the biological conditions and other genes. Gene
microarray experiment identifies the gene expression data for
a particular condition and varying time periods. Identifying
such network will lead to various applications in biological
and medical areas. Objective of this paper is to propose a new
method, which leads to substantial improvements in
processing time and accuracy. High dimensionality of the
microarray data matrix makes the identification of GRN
complex. In this paper optimization of S-system model using
artificial immune system is proposed.
The rest of this paper is organized as follows. A brief survey
of some of the existing work is given in Section 2. Section 3
presents the mathematical model used for the modeling of
gene regulatory network and algorithm for the optimization
2. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 08 | Aug-2013, Available @ http://www.ijret.org 45
process. Section 4 describes the experimental setup and
compares the results of the new proposal with the existing
approach. Section 5 is a discussion based on the results
obtained by the proposed method on the real life data set
called SOS Ecoli DNA repairing gene expression. Finally, the
paper is concluded in Section 6.
2. LITERATURE SURVEY
There have been several mathematical models applied for the
gene regulatory network reconstruction. One of the basic
mathematical models identified was based on Random
Boolean Network [1]. According to this model, the state of a
particular gene will be either in on or off state. The state space
for Boolean network is 2N
where N is the number of genes in
microarray. This model gives the information about gene
states, but does not provide expression levels of genes.
Zhang et al. [25] suggested Bayesian network model based on
joint probability distribution. This model uses DAG (Directed
Acyclic Graph) structure for modeling. Since the gene
regulatory network is having the property of cyclic
dependency between gene nodes, this type of model is not
efficient for inferring gene network.
Another important work [17] proposed is the modeling of
Gene regulatory network using ANN (Artificial Neural
Network) with the standard back propagation method. The
number of inputs and outputs required for this model is N,
where N is the number of genes in microarray data set. The
structural complexity of ANN model will increase as the
number of genes increases; hence, this model is not efficient
for large data sets.
Reverse engineering using the evolutionary algorithms can be
applied for solving the optimization problems. Genetic
algorithm is one of the major evolutionary algorithms that can
be used to construct the gene network. Spieth et al. [21]
proposed a memetic inference method for gene regulatory
network based on S-system. This is a popular mathematical
model proposed by Savageau [20]. The memetic algorithm
uses a combination of genetic algorithm and evolution
strategies [21].
A multi objective phenomic algorithm proposed by Rio
D’Souza et al. [8] is an advanced method, which concentrates
on multiple objectives like Number of Links (NoL) and Small
World Similarity Factor (SWSF). Rio DSouza et al. in [9]
proposes an Integrated Pheneto-Genetic Algorithm (IPGA),
which makes use of the approach of S-system model [20] with
memetic algorithm proposed by Spiethet al. [21]. The memetic
algorithm [21] makes use of genetic algorithm to identify the
populations of structures of possible networks. For N genes,
out of N combinations of solutions, GA is used to identify the
best solution by optimizing the error or fitness value. Memetic
algorithm is a superior method than the existing evolutionary
algorithms such as standard evolutionary strategy and
skeletalizing (extension of standard GA) for the particular
problem [21]
Nonetheless, the above algorithms are standard algorithms, the
tradeoff between time, space and accuracy factors of the
algorithms are still issues need to be addressed. In this paper,
we make a new proposal to optimize the model parameters for
the reconstruction of gene network for achieving improved
performance.
3. PROPOSED METHOD
3.1 MODEL
S-systems are a type of power law formalism, which was
suggested by Savageau [20] and defined as follows.
Where Gij and Hij are kinetic exponents, i and i are positive
rate constants and those values are optimized using Evolution
strategies. According to the S-system equation [1], 2N*(1+N)
values are to be optimized for each individual in a population,
where N is the total number of genes in a microarray data set.
We propose to employ an optimization technique known as
Clonal selection algorithm, which is faster than the genetic
algorithm. Clonal selection algorithm is a technique used in
artificial immune systems. A brief description of artificial
immune system and Clonal selection algorithm is given in the
following:
3.2 ARTIFICIAL IMMUNE SYSTEM (AIS)
Artificial Immune System is based on the theory of biological
immune system. In biological immune system, the foreign
materials, which are trying to intrude the body, will be
identified and prevented. These foreign materials are called
pathogens. Each pathogen has molecules called antigen which
will be identified by the antibody. There are two types of
immune systems in body called innate immune system and
adaptive immune system [2]. Innate immune system is a static
method, which is generic to all bodies. These are the basic
level of protection from pathogen [6]. Adaptive immune
systems are self-adaptive natured immunities, which work
with the antigens. This type of immunity remembers previous
attacks and strengthens the immunity process. In artificial
immune system, the principles of biological immune system
are used to solve the various computational problems. Clonal
selection is one of the theories, which explain the process of
immunity.
3.3 CLONAL SELECTION ALGORITHM
The response of immune system to infection explained by
Burnet is a well-known theory in immunology [4]. In this
3. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 08 | Aug-2013, Available @ http://www.ijret.org 46
work, Clonal selection is used to explain the processing of
adaptive immune system to antigens. In 2002, Castro and
Zuben proposed a Clonal based algorithm called CLONALG
[6]. Clonal selection algorithms follow the biological adaptive
immune system, which consists of antibodies and antigens [2].
This type of algorithms considers solution set as antibody. The
set of antibodies is called as population. At each generation
selection, cloning, affinity maturation and reselection are
happening to the population and trying to generate new
population with better affinity. In this algorithm, affinity is
calculated with the help of fitness value. As there are no
recombination/ crossover steps in Clonal selection algorithm,
it is faster than the genetic algorithm and hence the basic
Clonal selection algorithm is used to optimize the S-systems
model. The Clonal algorithm for the optimization of the S-
systems model is given below:
Algorithm 1: CLONAL based Algorithm
Require: Max N of Generation; error tolerance
Ensure: Optimal antibody
a. Start.
b. Generation: = 0
c. Pop(Generation) := Init(Clonal pop)
d. Evaluate_Fitness (Pop (Generation))
e. while termination criteria not met do
i. Selected_Pop(Generation):=Selection(Pop(Generatio
n))
ii. Cloned_Pop(Generation):=Clone(Selected_Pop(Gene
ration)
iii. Pop(Generation):=Maturation(Cloned_pop(Generatio
n))
iv. Evaluate_Fitness (Pop(Generation)
v. Pop(Generation+1):=Re_Selection(Pop(Generation))
vi. Generation := Generation + 1
f. end while
g. Stop.
Fitness function: The proposed method uses the following
fitness function proposed by Tominaga et al. [23]:
Where Xcal
i,t, Xexp
i,t are the expression value of gene i at time t
from the estimated (calculated) and experimental data
respectively.
4. EXPERIMENTAL SET UP AND RESULTS
For the experimentation, the standard artificial gene regulatory
network, given in Table 1, used by various researchers [12, 13,
14, 16, 18, 19] is made use of. This network consists of 5
genes. The Runge-kutta algorithm is used to infer standard
microarray data using the S-system model [13]. In order to
confirm the ability of proposed method to infer the gene
regulatory network we generated 10 sets of expression data
artificially. Initial values of these sets are randomly generated
in the range [0, 1] as shown in Table 2.The 10 sets of time
series data are obtained using equation(1) and S-system
parameters given in Table 1,with T=11 and G=5; so totally
10*11*5=550expression values are observed. A sample Time
dynamics of the 5 dimensional regulatory system inferred is
shown in Fig.1where duration of 0.0 to 0.5 is divided into 11
equi-distance samples, and 10 points are computed between
each sampling point.
In order to confirm the effectiveness of the proposed model,
both the proposed algorithm and the standard memetic
algorithm have been implemented and applied to a standard
artificial genetic network [12, 13, 14, 16, 18, 19]. Since these
algorithms are stochastic in nature, we have to test on multiple
data sets for the experiment. After computing the model
parameters, the microarray data set is regenerated and
compared with the original. We have used350000 fitness
evaluations in the comparative study. Mean Squared Error
(MSE) [23] is used as the error evaluation measurement
metric.
Fig. 1: A sample Time dynamics of the 5-dim regulatory
system using parameters in Table 1.
Fig. 2 shows comparison of average error (MSE) versus
fitness evaluation courses obtained for memetic and proposed
method for 3.5 lakhs fitness evaluation. Since memetic
algorithm uses genetic algorithm for the optimization purpose,
over all error will be reduced after some iterations. In memetic
algorithm, S-system parameters are optimized for the
reconstruction of gene regulatory network. In this algorithm
for each generation in genetic algorithm, evolutionary strategy
with covariance matrix adaptation (CMA) has to be
performed. Evolutionary strategy is a local optimal
evolutionary algorithm, which is much similar to genetic
algorithm. Due to hybrid nature of the algorithm, huge amount
of computation is required for the processing. For the memetic
algorithm, convergence happens after 20 lakhs fitness
evaluations [21]. The proposed method converged after 3.5
lakhs fitness evaluations whereas, at this point, standard
memetic algorithm is far away from convergence. Hence, it is
also observed that the proposed algorithm converges much
faster than the existing memetic algorithm
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Volume: 02 Issue: 08 | Aug-2013, Available @ http://www.ijret.org 48
Fig. 2: Comparison of average error (MSE) obtained for
memetic algorithm and the proposed approach; the proposed
algorithm converges at about 3.5 lakhs fitness evaluations.
5. DISCUSSION
5.1 ANALYSIS OF REAL LIFE DATA USING THE
PROPOSED METHOD
In order to assure the performance of a method, it should be
evaluated on a real life data. We employed a famous real life
dataset called SOS DNA repair system in E.coli [22] to study
the performance of the proposed method. Fig.3 graphically
describes the interactions during the repairing of DNA of
E.coli., when DNA damage is occurred. According to this
system, when a damage happens immediately RecA protein
will identify the damage and will invoke the processing of
cleavage of LexA protein without any help of enzymes. Thus,
the concentration of LexA will be decreased. Due to reduction
of LexA other proteins in the SOS system will activate the
repairing process of the DNA. LexA protein is acting as a
repressor in the system. After the repairing of DNA,
concentration of RecA will be dropped; in effect, automatic
cleavage of RecA will stop. Finally, concentration of LexA
will increase and repress the other genes. This will lead to a
stable state and will continue in this state till the next damage
happens.
SOS Data is obtained from the website www.weizmann
.ac.il/mcb/UriAlon/Papers/SOSData/ as a result of
experiments done by Uri Alon lab of Weizmann institute of
science. They have 4experimental results obtained, each of 8
proteins and 50 time points. As the first time-point represents
0 seconds all initial expression values are zeros. Since the first
time-point contains no information it was removed and the
remaining 49 time points were used for the modeling. From
the previous literatures [3, 5, 10, 12, 15, 16] it was identified
that out of 8 genes, 6 major genes (uvrD, umuD, lexA,recA,
uvrA and polB) and last 2 experimental results are required for
the accurate prediction of SOS Gene regulatory system. Each
values of the gene expression values are normalized in the
interval [0,1].
Fig3. SOS DNA repair system of E.coli.
Implementation of the proposed approach on SOS Data set
inferred the gene network of Fig.4. Since in the given
microarray, data is real one it is concealed with noise, and
hence the accuracy of the proposed algorithm depends on the
degree of noise. As the biological systems are so complex,
even with the biological experiments it is difficult to extract
all the hidden facts in the system. Therefore, the SOS DNA
repair system of Ecoli identified in Fig. 4 may not contain all
the relationships. There is still a possibility of finding new
relationships. The gene network obtained by the proposed
method identified inhibitions from LexA to LexA, uvrD,
uvrA, recA and polB. Proposed method also identified
regulations from recA to recA and recA to lexA correctly.
There are also some more relations, as given in Table 3
reported by other researchers, identified by the proposed
method.
Table3. Relations identified by the proposed approach that are also already identified by previous researchers
Gene Predicted relation and the references where these are already identified
uvrD uvrD -| uvrD(12, 5, 15, 11), uvrD -| umuDc (15), uvrD -| lexA (15), uvrD→polB (10, 11)
LexA LexA-| LexA(3, 5, 10, 12, 16), LexA-| uvrD(5, 10, 12, 16), LexA-| recA(3, 12, 16), LexA-| uvrA(5, 10, 11, 12),
LexA→ uvrA(11, 15), LexA-| PolB(5, 11, 12, 16), LexA→ PolB(11, 15),
umuDc umuDc -| umuDc (3, 5, 15, 11), umuDc -| recA(16, 3, 11), umuDc -| polB(11), umuDc→uvrA(11), umuDc -|
lexA (3, 15, 11)
recA recA→uvrA(11), recA -| uvrA (15, 10), recA -| umuDc(12, 15, 10, 11)
uvrA uvrA -| uvrA (16, 12, 5, 15, 11), uvrA -| recA (11), uvrA -| umuDc (16, 10), uvrA -| lexA (15, 10),
uvrA→uvrD(16, 12, 10, 11), uvrA -| polB(16, 11)
polB polB -| polB(12, 5, 11), polB→uvrD (11), polB -| recA(16, 11), polB→uvrA (11), polB -| uvrA(11)
6. IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 02 Issue: 08 | Aug-2013, Available @ http://www.ijret.org 49
Therefore, out of the total 33 relations, 30 relations are already
proposed by previous researchers. The remaining may be the
relations, which were not found yet, or false positives. Hence,
it is demonstrated that the proposed algorithm can be used for
the real life applications.
Fig4. SOS DNA repair system of E.coli. Identified by the
proposed method (dashed lines indicate the inhibition and
solid lines indicate the activation); 33 relations are identified.
CONCLUSIONS
Gene regulatory network reconstruction is a major issue in
bioinformatics. Existing methods for GRN reconstruction
either take longer computations for convergence or poor in
accuracy of identifying the relations. This paper proposes a
Clonal based approach using S-system model. The model
parameters are computed using optimization employing the
basic Clonal selection algorithm. Performance of the model is
compared with the existing standard memetic algorithm and
found to be superior with respect to execution time and
accuracy. Convergence is achieved with much lesser number
of fitness evaluations than the standard memetic algorithm.
The results obtained on SOS DNA repair system of E.coli.
demonstrate that the proposed approach identified most of the
relations identified by the previous researchers. This amply
proves that the approach is powerful and applicable to real life
data.
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BIOGRAPHIES
Jereesh A S received Bachelor’s degree in
Computer science and engineering from the
Rajiv Gandhi Institute of technology Kottayam
in the year 2007 and received Master’s degree
in Computer science and engineering
(Information Security) from the National
Institute of technology Calicut in the year 2010. He is
currently a research scholar pursuing for Ph.D degree in the
Department of Computer science and engineering at National
institute of Technology Calicut. His research interests include
the Bioinformatics, data mining and evolutionary algorithms.
V K Govindan received Bachelor’s and
Master’s degrees in electrical engineering from
the National Institute of technology Calicut in
the year 1975 and 1978, respectively. He was
awarded PhD in Character Recognition from
the Indian Institute of Science, Bangalore, in
1989. His research areas include Image processing, pattern
recognition, data compression, document imaging and
operating systems. He has more than 125 research publications
in international journals and conferences, and authored ten
books. He has produced seven PhDs and reviewed papers for
many Journals and conferences. He has more than 34 years of
teaching experience at UG and PG levels and he was the
Professor and Head of the Department of Computer Science
and Engineering, NIT Calicut during years 2000 to 2005. He
is currently working as Professor in the Department of
Computer Science and Engineering, and Dean Academic at
National Institute of Technology Calicut, India