ABSTRACT
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain network interaction, protein- protein interaction and gene-gene interaction. A number of neurological research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the processed data to find gene interaction outputs. Then we filter the output files using probe set filtering attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the interaction network using GeneMania software.
Discuss about Al, machine learning, and the hype cycle
Discuss the knowledge-based classification of proteins
Discuss applications of AI/ML to drug discovery
Uses of Artificial Intelligence in BioinformaticsPragya Pai
This presentation is about the usage of Artificial Intelligence in Bioinformatics. These slides give the basic knowledge about usage of Artificial Intelligence in Bioinformatics.
A clonal based algorithm for the reconstruction of genetic network using s sy...eSAT Journals
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.
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.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Discuss about Al, machine learning, and the hype cycle
Discuss the knowledge-based classification of proteins
Discuss applications of AI/ML to drug discovery
Uses of Artificial Intelligence in BioinformaticsPragya Pai
This presentation is about the usage of Artificial Intelligence in Bioinformatics. These slides give the basic knowledge about usage of Artificial Intelligence in Bioinformatics.
A clonal based algorithm for the reconstruction of genetic network using s sy...eSAT Journals
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.
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.
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...ijaia
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
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.
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.
Drug discovery and development is a long and expensive process 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). 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 drug 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 becomes all the more important to accelerate 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. Classification 2. Regression 3. Read-across. The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of 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 also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.
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.
Functional Genomics Journal Club presentation on the following publication:
Kuzawa, C. W., Chugani, H. T., Grossman, L. I., Lipovich, L., Muzik, O., Hof, P. R., … Lange, N. (2014). Metabolic costs and evolutionary implications of human brain development. Proceedings of the National Academy of Sciences, 111(36), 13010–13015. https://doi.org/10.1073/pnas.1323099111
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
How is machine learning significant to computational pathology in the pharmac...Pubrica
• Plentiful amassing of advanced histopathological pictures has prompted the expanded interest for their examination; for example, PC supported determination utilizing AI procedures.
• In this blog, Pubrica explains the applications of machine learning in digital pathology field using Biostatistics Services.
Continue Reading: https://bit.ly/37Vp6co
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|onTime|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Presentation about how much bioinformatics involved in the medical field. This was presented at the University of Colombo in 2007 for an undergraduate seminar
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Graphical Model and Clustering-Regression based Methods for Causal Interactio...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
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.
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.
Drug discovery and development is a long and expensive process 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). 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 drug 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 becomes all the more important to accelerate 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. Classification 2. Regression 3. Read-across. The talk will also cover how by using a hierarchical classification methodology you can simplify the problem of assessing toxicity of any given chemical compound. We will also address recent progress of 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 also address some of the remaining challenges and limitations yet to be addressed in the area of drug safety assessment.
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.
Functional Genomics Journal Club presentation on the following publication:
Kuzawa, C. W., Chugani, H. T., Grossman, L. I., Lipovich, L., Muzik, O., Hof, P. R., … Lange, N. (2014). Metabolic costs and evolutionary implications of human brain development. Proceedings of the National Academy of Sciences, 111(36), 13010–13015. https://doi.org/10.1073/pnas.1323099111
EG-CompBio presentation about Artificial Intelligence in Bioinformatics covering:
-AI (Types, Development)
-Deep Learning (Architecture)
-Bioinformatics Fields
-Input formats for AI
-AI Challenges in Biology
-Example: (Proteomics, Transcriptomics)
-Metagenomics: @ NU
-Taxonomic Classification
-Phenotype Classification
-How to begin in AI in Bioinformatics
How is machine learning significant to computational pathology in the pharmac...Pubrica
• Plentiful amassing of advanced histopathological pictures has prompted the expanded interest for their examination; for example, PC supported determination utilizing AI procedures.
• In this blog, Pubrica explains the applications of machine learning in digital pathology field using Biostatistics Services.
Continue Reading: https://bit.ly/37Vp6co
Reference: https://pubrica.com/services/research-services/biostatistics-and-statistical-programming-services/
Why Pubrica?
When you order our services, Plagiarism free|onTime|outstanding customer support|Unlimited Revisions support|High-quality Subject Matter Experts.
Contact us :
Web: https://pubrica.com/
Blog: https://pubrica.com/academy/
Email: sales@pubrica.com
WhatsApp : +91 9884350006
United Kingdom: +44- 74248 10299
Presentation about how much bioinformatics involved in the medical field. This was presented at the University of Colombo in 2007 for an undergraduate seminar
GRAPHICAL MODEL AND CLUSTERINGREGRESSION BASED METHODS FOR CAUSAL INTERACTION...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast Cancer at the early stage was to apply artificial intelligence where machines are simulated with intelligence and programmed to think and act like a human. This allows machines to passively learn and find a pattern, which can be used later to detect any new changes that may occur. In general, machine learning is quite useful particularly in the medical field, which depends on complex genomic measurements such as microarray technique and would increase the accuracy and precision of results. With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast Cancer diagnostic system using complex genomic analysis via microarray technology. The system will combine two machine learning methods, K-means cluster, and linear regression.
Graphical Model and Clustering-Regression based Methods for Causal Interactio...gerogepatton
The early detection of Breast Cancer, the deadly disease that mostly affects women is extremely complex
because it requires various features of the cell type. Therefore, the efficient approach to diagnosing Breast
Cancer at the early stage was to apply artificial intelligence where machines are simulated with
intelligence and programmed to think and act like a human. This allows machines to passively learn and
find a pattern, which can be used later to detect any new changes that may occur. In general, machine
learning is quite useful particularly in the medical field, which depends on complex genomic
measurements such as microarray technique and would increase the accuracy and precision of results.
With this technology, doctors can easily diagnose patients with cancer quickly and apply the proper
treatment in a timely manner. Therefore, the goal of this paper is to address and propose a robust Breast
Cancer diagnostic system using complex genomic analysis via microarray technology. The system will
combine two machine learning methods, K-means cluster, and linear regression.
Technology R&D Theme 2: From Descriptive to Predictive NetworksAlexander Pico
National Resource for Networks Biology's TR&D Theme 2: Genomics is mapping complex data about human biology and promises major medical advances. However, the routine use of genomics data in medical research is in its infancy, due mainly to the challenges of working with highly complex “big data”. In this theme, we will use network information to help organize, analyze and integrate these data into models that can be used to make clinically relevant diagnoses and predictions about an individual.
Genome feature optimization and coronary artery disease prediction using cuck...CSITiaesprime
Cardiovascular diseases are among the major health ailment issue leading to millions of deaths every year. In recent past, analyzing gene expression data, particularly using machine learning strategies to predict and classify the given unlabeled gene expression record is a generous research issue. Concerning this, a substantial requirement is feature optimization, which is since the overall genes observed in human body are closely 25000 and among them 636 are cardiovascular related genes. Hence, it complexes the process of training the machine learning models using these entire cardiovascular gene features. This manuscript uses bidirectional pooled variance strategy of ANOVA standard to select optimal features. Along the side to surpass the constraint observed in traditional classifiers, which is unstable accuracy at k-fold cross validation, this manuscript proposed a classification strategy that build upon the swarm intelligence technique called cuckoo search. The experimental study indicating that the number of optimal features those selected by proposed model is substantially low that compared to the other contemporary model that selects features using forward feature selection and classifies using support vector machine classifier (FFS&SVM). The experimental study evinced that the proposed model, which selects feature by bidirectional pooled variance estimation and classifies using proposed classification strategy that build on cuckoo search (BPVE&CS) outperformed the selected contemporary model (FFS&SVM).
Optimizing Alzheimer's disease prediction using the nomadic people algorithm IJECEIAES
The problem with using microarray technology to detect diseases is that not each is analytically necessary. The presence of non-essential gene data adds a computing load to the detection method. Therefore, the purpose of this study is to reduce the high-dimensional data size by determining the most critical genes involved in Alzheimer's disease progression. A study also aims to predict patients with a subset of genes that cause Alzheimer's disease. This paper uses feature selection techniques like information gain (IG) and a novel metaheuristic optimization technique based on a swarm’s algorithm derived from nomadic people’s behavior (NPO). This suggested method matches the structure of these individuals' lives movements and the search for new food sources. The method is mostly based on a multi-swarm method; there are several clans, each seeking the best foraging opportunities. Prediction is carried out after selecting the informative genes of the support vector machine (SVM), frequently used in a variety of prediction tasks. The accuracy of the prediction was used to evaluate the suggested system's performance. Its results indicate that the NPO algorithm with the SVM model returns high accuracy based on the gene subset from IG and NPO methods.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Evaluation of Logistic Regression and Neural Network Model With Sensitivity A...CSCJournals
Logistic Regression (LR) is a well known classification method in the field of statistical learning. It allows probabilistic classification and shows promising results on several benchmark problems. Logistic regression enables us to investigate the relationship between a categorical outcome and a set of explanatory variables. Artificial Neural Networks (ANNs) are popularly used as universal non-linear inference models and have gained extensive popularity in recent years. Research activities are considerable and literature is growing. The goal of this research work is to compare the performance of Logistic Regression and Neural Network models on publicly available medical datasets. The evaluation process of the model is as follows. The logistic regression and neural network methods with sensitivity analysis have been evaluated for the effectiveness of the classification. The Classification Accuracy is used to measure the performance of both the models. From the experimental results it is confirmed that the neural network model with sensitivity analysis model gives more efficient result.
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
Read More : http://bit.ly/2Rl7XT5
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Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral...ijtsrd
This paper presents the development of a hybrid dynamic expert system for the diagnosis of peripheral diabetes and remedies using a rule based machine learning technique. The aim was to develop a solution to the risk factors of peripheral diabetes. The methodology applied in this study is the experimental method, and the software design methodology used was the agile methodology. Data was collected from Nnamdi Azikiwe University Teaching Hospitals NAUTH and the Lagos State University Teaching Hospital LASUTH for patients between the ages of 28 87years suffering from peripheral neuropathy. Other methods used were data integration by applying uniform data access UDA technique, data processing using Infinite Impulse Response Filter IIRF , data extraction with a computerized approach, machine learning algorithm with Dynamic Feed Forward Neural Network DFNN , rule base algorithm. The modeling of the hybrid dynamic expert system and remedies was achieved using the DFNN for the detection of DPN and a rule based model for remedies and recommendations. The models were implemented with MATLAB and Java programming languages. The result when evaluated achieved a Mean Square Error MSE of 4.9392e 11 and Regression R of 0.99823. The implication of the result showed that the peripheral diabetes detection model correctly learns the peripheral diabetes attributes and was also able to correctly detect peripheral diabetes in patients. The model when compared with other sophisticated models also showed that it achieved a better regression score. The reason was due to the appropriate steps used in the data preparation such as integration and the use of IIFR filter, feature extraction, and the deep configuration of the regression model. Omeye Emmanuel C. | Ngene John N. | Dr. Anyaragbu Hope U. | Dr. Ozioko Ekene | Dr. Iloka Bethram C. | Prof. Inyiama Hycent C. "Development of a Hybrid Dynamic Expert System for the Diagnosis of Peripheral Diabetes and Remedies using a Rule-Based Machine Learning Technique" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-7 , December 2022, URL: https://www.ijtsrd.com/papers/ijtsrd52356.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/52356/development-of-a-hybrid-dynamic-expert-system-for-the-diagnosis-of-peripheral-diabetes-and-remedies-using-a-rulebased-machine-learning-technique/omeye-emmanuel-c
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms.This technique gives an accuracy of 98%.
EFFICACY OF NON-NEGATIVE MATRIX FACTORIZATION FOR FEATURE SELECTION IN CANCER...IJDKP
Over the past few years, there has been a considerable spread of microarray technology in many
biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon
cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies
in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in
their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms
and approaches for the reduction of dimensionality of such microarray datasets.This study exploits the
matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix
Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification
accuracies are then compared for these algorithms.This technique gives an accuracy of 98%
CLASSIFICATION OF ALZHEIMER USING fMRI DATA AND BRAIN NETWORKcscpconf
Since the mid of 1990s, functional connectivity study using fMRI (fcMRI) has drawn increasing
attention of neuroscientists and computer scientists, since it opens a new window to explore
functional network of human brain with relatively high resolution. BOLD technique provides
almost accurate state of brain. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of
neurological research paper also analyse the relationship among damaged part. By
computational method especially machine learning technique we can show such classifications.
In this paper we used OASIS fMRI dataset affected with Alzheimer’s disease and normal
patient’s dataset. After proper processing the fMRI data we use the processed data to form
classifier models using SVM (Support Vector Machine), KNN (K- nearest neighbour) & Naïve
Bayes. We also compare the accuracy of our proposed method with existing methods. In future,
we will other combinations of methods for better accuracy.
Application of Microarray Technology and softcomputing in cancer BiologyCSCJournals
DNA microarray technology has emerged as a boon to the scientific community in understanding the growth and development of life as well as in widening their knowledge in exploring the genetic causes of anomalies occurring in the working of the human body. microarray technology makes biologists be capable of monitoring expression of thousands of genes in a single experiment on a small chip. Extracting useful knowledge and info from these microarray has attracted the attention of many biologists and computer scientists. Knowledge engineering has revolutionalized the way in which the medical data is being looked at. Soft computing is a branch of computer science capable of analyzing complex medical data. Advances in the area of microarray –based expression analysis have led to the promise of cancer diagnosis using new molecular based approaches. Many studies and methodologies have come up which analyszes the gene espression data by using the techniques in data mining such as feature selection, classification, clustering etc. emboiding the soft computing methods for more accuracy. This review is an attempt to look at the recent advances in cancer research with DNA microarray technology , data mining and soft computing techniques.
Similar to GENE-GENE INTERACTION ANALYSIS IN ALZHEIMER (20)
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
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SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
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Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
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The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
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gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
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Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
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systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
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questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
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and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
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In the realm of computer security, the importance of efficient and reliable user authentication methods has
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comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
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The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
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The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
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machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
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In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
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susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this
paper, we explore the security of key components in the EV charging infrastructure, including the mobile
application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack
between an EV app and its cloud services. Our results showed that it is possible to launch attacks against
the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and
operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions
and future research directions.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This paper describes the outcome of an attempt to implement the same transitive closure (TC) algorithm
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When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
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Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Water Industry Process Automation and Control Monthly - May 2024.pdf
GENE-GENE INTERACTION ANALYSIS IN ALZHEIMER
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
DOI: 10.5121/ijcsit.2018.10307 113
GENE-GENE INTERACTION ANALYSIS IN
ALZHEIMER
Rishi Yadav, Ravi Bhushan Mishra
Computer Science & Engineering, IIT BHU (Varanasi)
ABSTRACT
Genome-wide transcription profiling is a powerful technique in studying disease susceptible footprints.
Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene
expression that give information on the context or underlying basis for the disease and may provide a new
diagnostic approach. However, the data obtained from high-density microarrays is highly complex and
poses considerable challenges in data mining. Past researches prove that neuro diseases damage the brain
network interaction, protein- protein interaction and gene-gene interaction. A number of neurological
research paper also analyze the relationship among damaged part. Analysis of gene-gene interaction
network drawn by using state-of-the-art gene database of Alzheimer’s patient can conclude a lot of
information. In this paper we used gene dataset affected with Alzheimer’s disease and normal patient’s
dataset from NCBI databank. After proper processing the .CEL affymetrix data using RMA, we use the
processed data to find gene interaction outputs. Then we filter the output files using probe set filtering
attributes p-value and fold count and draw a gene-gene interaction network. Then we analyze the
interaction network using GeneMania software.
KEYWORDS
Gene-gene interaction, Dementia, Alzheimer’s disease, RMA, GeneMania
1. INTRODUCTION
Alzheimer’s disease (AD) is a chronic, incurable, irreversible neurodegenerative disorder and
multifaceted disease which along with other neurodegenerative diseases, represents the largest
area of unmet need of modern medical science[2]. The cognitive decline caused by this disorder
leads to behavioral change, loss of memory and thinking skills and ultimately effects the simpler
task performance. The disease begins with mild deterioration and ultimately worse in
neurodegenerative type of dementia. Diagnosis of Alzheimer’s disease is possible but it is very
costly, time-taken and medically infeasible. It requires very careful medical assessment such as
patient history, behavioral pattern analysis and different physical and neurobiological exam.
Bioinformatics field providing a great support to modern medical science. Bioinformatics is the
application of tools of computation and to the capture and interpretation of biological data. A lot
of computational methods have been used to assist modern biology. Computational tools are very
efficient in recognizing pattern of diseases, handing big sample data, analyzing and interpreting
the sample data. Neurodegenerative diseases like Alzheimer’s disease (AD), Parkinson’s diseases
etc. fallows a great patterns in patients, which can be analyzed by machine learning techniques,
computational tools and software.
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
114
Alzheimer’s disease neuronal dysfunction mainly causes due to failure of functional integration
and damage in neural connectivity network [8]. Brain is the center of human nervous system
which is made of 100 billion nerves that communicate in trillions of connections called synapses.
In past three decades, a rich study on recognizing neural pattern have provided plenty of
knowledge about defected neural connection in AD. A number of computational tools and
software have developed to collect this information [7].
Sarraf et al.,[8] used convolutional neural network (CNN) to classify Alzheimer brain and
normal healthy brain. They used MRI slices of Alzheimer patient and normal healthy brain from
ADNI databank and pre-processed the image using the standard modules of FMRIB library v5.0.
Images were labelled for binary classification of Alzheimer’s Vs normal dataset and these
labelled images were converted to Imdb storage databases for higher throughput to be fed into
Deep learning platform. The study used CNN and famous architecture LeNet-5 and successfully
classified fMRI data of Alzheimer’s patient from normal controls. The accuracy of test data on
training data reached 96.85 %, which was trained and tested with huge number of images. This
study also showed a novel path to use more complicated computational architecture and
classifiers to increase the efficient and effective pre-clinical diagnosis of AD.
There are lot of genetic factors that influence common and complex traits human behavior. The
characterization of the effects of those factors is both a goal and challenge for modern geneticists.
In the last couple of years, the field has been revolutionized by the success of genome-wide
association (GWA) studies [10] [11]. Gene is a basic physical and functional unit of heredity.
Gene made up of DNA, act as instructions to make molecules called protein. The one goal of
modern geneticists is to identify genes with specific DNA sequence variations that increase or
decrease susceptibility of disease [5]. In past years many researches focused on finding genetic
architecture of diseases. Gene-gene interaction or epistasis is a ubiquitous component of the
genetic architecture of common human diseases. Musameh et al., [4] aimed to investigate the
role of gene-gene interactions in common variants in candidate cardiovascular genes in coronary
artery disease (CAD).
Meng et al., [3] explored gene-gene interactions that have the potential to partially fill the
missing heritability. Though in the previous studies interesting genes have been found, they
identified such interesting genes which had no strong main effect on hypertension but which
having indirect effect on hypertension prone genes. The paper provides evidence that the genome-
wide gene-gene interaction analysis has the possibility to identify new susceptibility genes, which
can provide more knowledge and insights into the genetic pattern of blood pressure regulation
and its effect on hypertension.
Gilbert-Diamond et al., [5] introduced the basics and importance of gene-gene interaction in
identifying disease susceptibility genes. The paper analyzed several statistical and computational
methods for characterizing and detecting genes with effects that are dependent on other genes.
They focused on genetic association studies of discrete and quantitative traits because most of the
methods for detecting gene-gene interactions have been developed by using these methods. The
paper introduced the gene-gene interaction, use of gene-gene interaction and challenges in this
research field. They described methods for detecting gene-gene interaction in association studies
of discrete traits like logistic regression and multifactor dimensionality reduction. In association
studies of quantitative traits methods like linear regression and combinational partitioning method
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
115
also discussed. Lastly paper also proved the advantage of genome wide approach in place of
traditional candidate-gene approach.
Gene-phenotype relationship is so complex that machine learning approaches have a considerable
appeal as a strategy foe modelling interactions. A number of such methods have been developed
and applied in recent years with some modest success. Koo et al., [1] presented an overview of
several machine learning techniques to solve gene-gene interaction problem in genetic
epidemiology. Traditional statistical methods are not efficient due to the curse of high
dimensionality of data and occurrence of multiple polymorphism. This paper gives an overview
of machine learning techniques support vector machine (SVM), random forest (RFs) and neural
networks (NNs) and its application in detecting gene environment interactions. They also
analyzed machine learning techniques which are most suitable to implement on gene-gene
interaction like genetic programming neural network (GPNN), back propagation neural network
(BPNN), grammatical evolution neural network (GENN), Recursive feature addition (SVM-
RFA), Recursive feature elimination (SVM-RFE), local search (SVM- Local), genetic algorithm
(SVM- GA) and mutual information network guided RF method (MINGRF). Lastly this paper
also discussed the strengths and weakness of all the variants of Support vector machine, random
forest and neural networks in implementing and detecting gene-gene interaction environment in
complex human diseases.
Nowadays genome-wide association studies have offered thousands of single nucleotide
polymorphism (SNPs). This study is important to unravel the genetic basis to complex
multifactorial diseases. The greatest challenge is to discover gene-gene interactions among large
amount of data having multiple polymorphisms. Thus various promising software have been
developed for epistasis interactions. Sirava et al., [2] developed a tool for modeling, analyzing
and visualizing biochemical pathways and networks of genomes and array data. The software tool
BioMiner is based on new comprehensive, extensible and reusable data model called BioCore.
The paper presented two applications, PathFinder and PathViewer. PathFinder predicting
biochemical pathways by comparing groups of related organisms based on sequence similarity
and successfully tested in number of experiments. PathViwer is an application for visualizing
metabolic networks and supports the graphical comparison of metabolic networks of different
organisms. Koo et al., [12] gives an overview on the software that had been used to detect gene-
gene interactions that bring the effect on common and multifactorial diseases. Lastly in this paper
sources, link, strength and weakness of the software that has been widely used in detecting
epistasis interactions in complex human diseases also analyzed.
We used state-of-the-art gene dataset id GDS4758 from The National Centre for Biotechnology
Information (NCBI), which provides free access to biomedical and genomic datasets. Dataset
consists of 79 sample counts postmortem brain tissues pathologically diagnosed as having AD
and normal patient. Dataset is in .CEL affymatrix format. We used most efficient method for pre-
processing of microarray data, normalization in R using Robust Multi-array average or Robust
Multi-chip average (RMA) method. We use AltAnalyze software for microarray data processing,
which includes several basic expression. Statistics are comprised of following steps- rawp, adjp,
log-fold, fold change, ANOVA rawp, ANOVA adjp and max log-fold. Then we use probe set
filtering in output files of AltAnalyze using p-value (<0.05) and fold count (>2). We get 20
interesting genes. Then we analyze 20 interesting gene network using GeneMania and draw
relevant conclusions from these gene-gene interaction network.
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
116
The paper’s organization is as fallows. Apart from introduction, Section II deals with problem
description, Section III deals with proposed methods including data acquisition, data pre-
processing, microarray analysis, Section IV deals with results description and Section V
represents Conclusion & Discussion.
2. PROBLEM DESCRIPTION
Early diagnosis of neuro disease like Alzheimer’s disease (AD) via medical assessment is very
costly and time consuming. Gene-gene interaction or epistasis is a ubiquitous component of the
genetic architecture of neuro disease. The purpose of this paper is to analyze some interesting
findings from gene-gene interaction network of AD patient. We use gene dataset GDS4758 from
NCBI as input to gene-gene interaction network.
3. PROPOSED METHOD
3.1 DATA ACQUISITION
The National Centre for Biotechnology information (NCBI) provided access to biomedical and
genomic datasets and related information. We take dataset record id GDS4758 from
(https://www.ncbi.nlm.nih.gov/geo/download/?acc=GDS4758). This dataset having 79 sample
counts of postmortem brain tissues from male and female Hisayama residents pathologically
diagnosed as having Alzheimer’s disease (AD). Dataset also having normal brain tissues data.
Dataset is collected by using the Affymetrix scanner software, which produces a number of file
when a gene chip is scanned. Two of these are .CHP and the .CEL files. Dataset GDS4758
contains .CEL files that contains probe level intensities. It contains the data extracted from probes
on an Affymetrix gene chip and can store thousands of data points. We used GDS4748 .CEL
format 79 sample counts as input dataset.
3.2 DATA PRE-PROCESSING
Microarray gene expressions are little noisy. There are many sources of noise in microarray
experiments like different amount of RNA used for labelling and hybridization, imperfectness on
the array surface, imperfect synthesis of the probes and difference in hybridization conditions etc.
Systematic differences due to noise in place of true biological variability should be removed in
order to make biologically meaningful conclusions about the data. There are many techniques for
the pre-processing of microarray data. R hasan et al., [13] discussed some of the most important
preprocessing techniques applicable to Affymetrix microarray platform.
One of the most efficient method for pre-processing microarray data is normalization in R using
Robust Multi-array average or Robust Multi-chip average (RMA). RMA method for computing
an expression measure begins by computing back-ground correlated perfect match intensities for
each perfect match cell on every GeneChip. Background-corrected intensity is calculated for each
PM probe in such a way that all background corrected intensities are positive. After that base-2
logarithm of the background- corrected intensity is calculated for each probe. This will male data
more skewed and more normally distributed and provide an equal spread of up and down
regulated expression ratios. Next step is for quantile normalization to correct for variation
between the arrays. It equalizes the data distributions of the arrays and make the samples
5. International Journal of Computer Science &
completely comparable. After that last st
between probe sets, which equalizes the behavior of the probes between the arrays and combines
normalized data values of probes from a probe set into a single value for the whole probe set. We
use R studio for RMA normalization of .CEL sam
3.3 MICROARRAY ANALYSIS
Microarray gene analysis comprises a number of computational steps. Such analysis having huge
amount and diversity of data. There are number of software which analyze
interactions and produces interaction outputs. We used AltAnalyze
and open-source analysis toolkit that can be used for a broad range of genomics ana
all Altanalyze also normalize affymetrix
computed based on corresponding probe set annotations in the downloaded CDF file which is
automatically recognized and downloaded by AltAnalyze.
3.3.1 GENE EXPRESSION ANALYSIS
Several basic expression statistics are performed for any user specified pairwise comparisons
(e.g., AD versus normal) and between all groups in the user dataset. These statistics are
comprised of following steps- rawp, adjp, log
and max log-fold. Log-fold is log2 fold calculated by geometric subtraction of the experimental
from the control groups for each pairwise comparison of AD and normal. The fold change is non
log2 transformed fold value. The max log
group mean and the highest group expression mean. The rawp is one one
variance p-value calculated for each pairwise comparisons.
3.3.2 RECIPROCAL JUNCTION ANALYSIS
Alternative exon regulation is determined by comparing normalized expression of one exon in
two or more conditions for an exon
of an individual divided by the gene
junctions or pair of exon-junctions to identify alternative exons from junction analysis, where one
measuring the inclusion of an exon and the other measuring exclusion of the exon. Altanalyze
used ASPIRE, Linear regression and Ri
Figure
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
completely comparable. After that last step is probe normalization to correct for variation
between probe sets, which equalizes the behavior of the probes between the arrays and combines
normalized data values of probes from a probe set into a single value for the whole probe set. We
for RMA normalization of .CEL sample counts of GDS4758.
comprises a number of computational steps. Such analysis having huge
amount and diversity of data. There are number of software which analyze microarray gene
interactions and produces interaction outputs. We used AltAnalyze - an extremely user friendly
source analysis toolkit that can be used for a broad range of genomics ana
all Altanalyze also normalize affymetrix .CEL files using RMA and expression values are
computed based on corresponding probe set annotations in the downloaded CDF file which is
automatically recognized and downloaded by AltAnalyze.
NALYSIS
tics are performed for any user specified pairwise comparisons
(e.g., AD versus normal) and between all groups in the user dataset. These statistics are
rawp, adjp, log-fold, fold change, ANOVA rawp, ANOVA adjp
fold is log2 fold calculated by geometric subtraction of the experimental
for each pairwise comparison of AD and normal. The fold change is non
log2 transformed fold value. The max log-field means the log2 fold value between
group mean and the highest group expression mean. The rawp is one one-way analysis of
value calculated for each pairwise comparisons.
UNCTION ANALYSIS
Alternative exon regulation is determined by comparing normalized expression of one exon in
two or more conditions for an exon-level analysis. Normalized expression means the expression
of an individual divided by the gene-expression value. Altanalyze first identifies reciprocal
junctions to identify alternative exons from junction analysis, where one
measuring the inclusion of an exon and the other measuring exclusion of the exon. Altanalyze
used ASPIRE, Linear regression and Ri-PSI algorithms for reciprocal junction analysis.
Figure-1 Flowchart of Proposed method
Information Technology (IJCSIT) Vol 10, No 3, June 2018
117
ep is probe normalization to correct for variation
between probe sets, which equalizes the behavior of the probes between the arrays and combines
normalized data values of probes from a probe set into a single value for the whole probe set. We
comprises a number of computational steps. Such analysis having huge
array gene-gene
an extremely user friendly
source analysis toolkit that can be used for a broad range of genomics analysis. First of
CEL files using RMA and expression values are
computed based on corresponding probe set annotations in the downloaded CDF file which is
tics are performed for any user specified pairwise comparisons
(e.g., AD versus normal) and between all groups in the user dataset. These statistics are
fold, fold change, ANOVA rawp, ANOVA adjp
fold is log2 fold calculated by geometric subtraction of the experimental
for each pairwise comparison of AD and normal. The fold change is non-
field means the log2 fold value between the lowest
way analysis of
Alternative exon regulation is determined by comparing normalized expression of one exon in
Normalized expression means the expression
t identifies reciprocal
junctions to identify alternative exons from junction analysis, where one
measuring the inclusion of an exon and the other measuring exclusion of the exon. Altanalyze
SI algorithms for reciprocal junction analysis.
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118
3.4 PROBE-SET FILTERING
During the process of routine analysis of microarray gene interactions, a multiple testing
adjustment is certainly warranted due to large number of hypothesis test. Filtering allows us for
the reduction in number of tests and a corresponding increase in analysis power. We use p- value,
midas p-value, fold count and si p-value as a filter during microarray analysis.
p-value helps in determining significance of microarray gene-gene interaction analysis. P-value is
the level of marginal significance within a statistical hypothesis test representing the probability
of the occurrence of the given event. In most of the microarray analysis p-value is set to be (<
0.05). p-value (< 0.05) indicates strong evidence against the null hypothesis, so we can reject the
null hypothesis. A large p-value (>0.05) indicates weak evidence against the null hypothesis, so
we will fail to reject the null hypothesis. Thus p-value approach to hypothesis testing uses the
calculated probability to determine whether there is evidence to reject the null hypothesis. In our
research we set p-value thresholding as p-value <0.05.
Microarray detection of alternative splicing (Midas) p-value calculate an alternative splicing
score based on Midas method. Si p-value means significant p-value. We used filtering limits of
<0.05 for both, Midas p-value and si p-value. Another important filter in microarray analysis is
fold change. Fold change is a measure describing how much a quantity changes going from an
initial to final value. As per [14] fold count (>1.5) or (>2) produces most interesting genes in
microarray analysis. Dalman et al., [15] analyzed different combinations of p-value and fold
counts in microarray analysis and concluded to use fold count (>2) and p-value (<0.05). We use
same filters thresholding in our research work.
4. RESULTS
Microarray gene-gene interaction analysis using Altanalyze produces a five output files in
delimited text format that can be opened in Microsoft Excel for better visualization. These files
consists of a lot of gene-gene interaction information of input .CEL dataset. File #1 reports gene
expression values for each sample and group in your probeset input expression file. The values
are derived from probe sets that align to regions of a gene that are common to all transcripts and
thus are informative for transcription (unless all probe sets are selected - see "Select expression
analysis parameters", above) and expressed above specified background levels. Along with the
raw gene expression values, statistics for each indicated comparison (mean expression, folds, t-
test p-values) will be included along with gene annotations for that array, including putative
microRNA binding sites. This file is analogous to the results file you would have with a typical,
non-exon microarray experiment and is saved to the folder "ExpressionOutput".
Results from files #2-5 are produced from all probe sets that may suggest alternative splicing,
alterative promoter regulation, or any other variation relative to the constitutive gene expression
for that gene (derived from comparisons file). Each set of results correspond to a single pair-wise
comparison (e.g., cancer vs. normal) and will be named with the group names you assigned
(groups file).
File #2 reports probe sets that are alternatively regulated, based on the user defined splicing-index
score and p-value. For each probe set several statistics, gene annotations and functional
predictions are provided. Files #4 and #5 report over-representation results for protein domains
7. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
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(or other protein features) and microRNA-binding sites, predicted to be regulated by AltAnalyze.
These files include over-representation statistics and genes associated with the different domains
or features predicted to be regulated.
File #6 includes the number of genes alternatively regulated, differentiatially expressed and the
mean number of protein residues differening between predicted alternative isoforms. This file is
useful in comparing results between different pair-wise comparison files. Originally file#2
contains 206 interesting genes analyzed from gene-gene interaction using Altanalysis. We use
filters to find out most interesting genes related to Alzheimer’s disease dataset. We set p-value
(<0.05), Midas p-value (<0.05), si p-value (<0.05) and fold count (>2). On imposing these filters
in file#2 , we get 20 most interesting genes related to gene-gene interaction analysis of
Alzheimer’s disease dataset GDS4758. Now we can draw and analyze the gene-gene interaction
network of these most interesting 20 genes to conclude some interesting findings. Koo et al., [12]
gives an overview of software for visualizing gene-gene network with their respected strength
and weaknesses.
We use GeneMania - a flexible, user-friendly web interface for generating hypothesis about gene
function, analyzing gene list and prioritizing genes for functional assays. GeneMania takes gene
symbols or NCBI gene IDs as input to draw network. We gave 20 Homo Sapiens gene symbols of
file#2 as input. All the 20 genes got validation from GeneMania database.
(Figure-2 Gene-gene interaction network drawn by using GeneMania Software. It consists of 20 interesting
genes (having cross-hatched circle) found after proper pre-processing, analysis and filtering. figure also
consists 20 relevant genes (having solid circle) having most connectivity with interesting genes)
We get gene-gene interaction network as seen in figure 2. This network consists of 40 genes. 20
genes (having cross-hatched circle) are the most interesting genes from the output file#2 and
remaining 20 genes (having solid circle) are relevant genes having most connectivity with
interesting genes. Different type of interactions like physical interactions, co-expression, shared
protein domains, pathways etc. are represented by different colored connector lines. We can
select or deselect to which interactions are displayed. Gene-gene interaction network for input
8. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
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genes having 33.6% physical interactions with relevant genes. This shows high physical
interaction of most interesting 20 genes. All 40 genes having 175 total connector links.
GeneMania also displays functions associated with genes in the network and their FDR and
coverage (as number of genes annotated with that function in the network versus number of genes
annotated with that function in the genome). In our gene-gene interaction network functions
triglyceride-rich lipoprotein particle remodeling and very low density particle remodeling are
most effected functions from these 20 interesting genes having coverage ratio 4 out of 11 genes.
GeneMania also provide facility of relocation and adjustment of network.
From GeneMania, we export the gene-gene interaction network related pdf and text format files.
File GeneMania report provides gene interaction details of the network with gene interaction
ranking. Relevant genes CLUL1, TGFB1I1 and PLD5 are top 3 interacting genes of the network.
5. DISCUSSION & CONCLUSION
We used state-of-the-art gene dataset id GDS4758 from The National Centre for Biotechnology
Information (NCBI), which provides free access to biomedical and genomic datasets. Dataset
consists of 79 sample counts postmortem brain tissues pathologically diagnosed as having AD
and normal patient. Dataset is in .CEL affymatrix format. We used most efficient method for pre-
processing of microarray data, normalization in R using Robust Multi-array average or Robust
Multi-chip average (RMA) method. We use AltAnalyze software for microarray data processing,
which includes several basic expression. Statistics are comprised of following steps- rawp, adjp,
log-fold, fold change, ANOVA rawp, ANOVA adjp and max log-fold. Then we use probe set
filtering in output files of AltAnalyze using p-value (<0.05) and fold count (>2). We get 20
interesting genes. Then we analyze 20 interesting gene network using GeneMania and draw
relevant conclusions from these gene-gene interaction network.
From the gene-gene interaction network using GeneMania we draw these conclusions- (a) Out of
20 interesting genes some genes are highly correlated with relevant genes. CLUL1, DSC1 and
TGFB1I1 are top 3 genes having highest correlation values. Thus these homo sapiens genes
required extra care and medical research. (b) Every genotype having special effects on phenotype
or body functions. Using GeneMania we analyze the most effected functions are triglyceride-rich
lipoprotein particle remodeling and very low-density lipoprotein particle remodeling, which
consists 4 interesting genes out of 11 genes. This can help in biological study of Alzheimer’s
disease.
REFERENCE
[1] Koo, Ching Lee, Mei Jing Liew, Mohd Saberi Mohamad, Mohamed Salleh, and Abdul Hakim. "A
review for detecting gene-gene interactions using machine learning methods in genetic
epidemiology." BioMed research international 2013 (2013).
[2] Šırava, M., T. Schäfer, Markus Eiglsperger, Michael Kaufmann, Oliver Kohlbacher, Erich Bornberg-
Bauer, and Hans-Peter Lenhof. "BioMiner—modeling, analyzing, and visualizing biochemical
pathways and networks." Bioinformatics 18, no. suppl_2 (2002): S219-S230.
9. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 3, June 2018
121
[3] Meng, Ying, Susan Groth, Jill R. Quinn, John Bisognano, and Tong Tong Wu. "An Exploration of
Gene-Gene Interactions and Their Effects on Hypertension." International journal of genomics 2017
(2017).
[4] Musameh, Muntaser D., William YS Wang, Christopher P. Nelson, Carla Lluís-Ganella, Radoslaw
Debiec, Isaac Subirana, Roberto Elosua et al. "Analysis of gene-gene interactions among common
variants in candidate cardiovascular genes in coronary artery disease." PloS one10, no. 2 (2015):
e0117684.
[5] Gilbert‐Diamond, Diane, and Jason H. Moore. "Analysis of gene‐gene interactions." Current
protocols in human genetics(2011): 1-14.
[6] Cordell, Heather J. "Detecting gene–gene interactions that underlie human diseases." Nature Reviews
Genetics 10, no. 6 (2009): 392.
[7] Biju, K. S., S. S. Alfa, Kavya Lal, Alvia Antony, and M. Kurup Akhil. "Alzheimer’s Detection Based
on Segmentation of MRI Image." Procedia Computer Science 115 (2017): 474-481.
[8] Sarraf, Saman, and Ghassem Tofighi. "Classification of alzheimer's disease using fmri data and deep
learning convolutional neural networks." arXiv preprint arXiv:1603.08631 (2016).
[9] Caspi, Avshalom, Ahmad R. Hariri, Andrew Holmes, Rudolf Uher, and Terrie E. Moffitt. "Genetic
sensitivity to the environment: the case of the serotonin transporter gene and its implications for
studying complex diseases and traits." Focus 8, no. 3 (2010): 398-416.
[10] Wellcome Trust Case Control Consortium. "Genome-wide association study of 14,000 cases of seven
common diseases and 3,000 shared controls." Nature 447, no. 7145 (2007): 661.
[11] Easton, Douglas F., Karen A. Pooley, Alison M. Dunning, Paul DP Pharoah, Deborah Thompson,
Dennis G. Ballinger, Jeffery P. Struewing et al. "Genome-wide association study identifies novel
breast cancer susceptibility loci." Nature 447, no. 7148 (2007): 1087.
[12] Koo, Ching Lee, Mei Jing Liew, Mohd Saberi Mohamad, Abdul Hakim Mohamed Salleh, Safaai
Deris, Zuwairie Ibrahim, Bambang Susilo, Yusuf Hendrawan, and Agustin Krisna Wardani.
"Software for detecting gene-gene interactions in genome wide association studies." Biotechnology
and bioprocess engineering 20, no. 4 (2015): 662-676.
[13] R Hasan, Ahmed, John E Pattison, and Alex Hariz. "Pre-processing of affymetrix gene chip
microarray data." Current Bioinformatics 5, no. 4 (2010): 270-279.
[14] Vaes, Evelien, Mona Khan, and Peter Mombaerts. "Statistical analysis of differential gene expression
relative to a fold change threshold on NanoString data of mouse odorant receptor genes." BMC
bioinformatics 15, no. 1 (2014): 39.
[15] Dalman, Mark R., Anthony Deeter, Gayathri Nimishakavi, and Zhong-Hui Duan. "Fold change and p-
value cutoffs significantly alter microarray interpretations." In BMC bioinformatics, vol. 13, no. 2, p.
S11. BioMed Central, 2012.