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.
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.
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Even if a drug is approved in market, it could be withdrawn due to safety problems.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.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
Novel modelling of clustering for enhanced classification performance on gene...IJECEIAES
Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expressiondata. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches.
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.
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.
National Resource for Networks Biology's TR&D Theme 3: Although networks have been very useful for representing molecular interactions and mechanisms, network diagrams do not visually resemble the contents of cells. Rather, the cell involves a multi-scale hierarchy of components – proteins are subunits of protein complexes which, in turn, are parts of pathways, biological processes, organelles, cells, tissues, and so on. In this technology research project, we will pursue methods that move Network Biology towards such hierarchical, multi-scale views of cell structure and function.
Novel modelling of clustering for enhanced classification performance on gene...IJECEIAES
Gene expression data is popularized for its capability to disclose various disease conditions. However, the conventional procedure to extract gene expression data itself incorporates various artifacts that offer challenges in diagnosis a complex disease indication and classification like cancer. Review of existing research approaches indicates that classification approaches are few to proven to be standard with respect to higher accuracy and applicable to gene expression data apart from unaddresed problems of computational complexity. Therefore, the proposed manuscript introduces a novel and simplified model capable using Graph Fourier Transform, Eigen Value and vector for offering better classification performance considering case study of microarray database, which is one typical example of gene expressiondata. The study outcome shows that proposed system offers comparatively better accuracy and reduced computational complexity with the existing clustering approaches.
A survey of heterogeneous information network analysisSOYEON KIM
A Survey of Heterogeneous Information Network Analysis
Chuan Shi, Member, IEEE,
Yitong Li, Jiawei Zhang, Yizhou Sun, Member, IEEE,
and Philip S. Yu, Fellow, IEEE
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2015
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.
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.
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.
Review on Computational Bioinformatics and Molecular Modelling Novel Tool for...ijtsrd
Advancement in science and technology has brought a remarkable change in the field of drug discovery. Earlier it was very difficult to predict the target for receptor but nowadays, it is easy and robust task to dock the target protein with ligand and binding affinity is calculated. Docking helps in the virtual screening of drug along with its hit identification. There are two approaches through which docking can be carried out, shape complementary and stimulation approach. There are many procedures involved in carrying out docking and all require different software's and algorithms. Molecular docking serves as a good platform to screen a large number of ligands and is useful in Drug-DNA studies. This review mainly focuses on the general idea of molecular docking and discusses its major applications, different types of interaction involved and types of docking. Rishabh Jain "Review on Computational Bioinformatics and Molecular Modelling: Novel Tool for Drug Discovery" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18914.pdf
http://www.ijtsrd.com/pharmacy/pharmacoinformatics/18914/review-on-computational-bioinformatics-and-molecular-modelling-novel-tool-for-drug-discovery/rishabh-jain
Efficiency of LSB steganography on medical information IJECEIAES
The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Usin...Kato Mivule
Kato Mivule, Claude Turner, "A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Using Machine Learning Classification as a Gauge", Procedia Computer Science, Volume 20, 2013, Pages 414-419, Baltimore MD, USA
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
A Codon Frequency Obfuscation Heuristic for Raw Genomic Data PrivacyKato Mivule
Genomic data provides clinical researchers with vast opportunities to study various patient ailments. Yet the same data contains revealing information, some of which a patient might want to remain concealed. The question then arises: how can an entity transact in full DNA data while concealing certain sensitive pieces of information in the genome sequence, and maintain DNA data utility? As a response to this question, we propose a codon frequency obfuscation heuristic, in which a redistribution of codon frequency values with highly expressed genes is done in the same amino acid group, generating an obfuscated DNA sequence. Our preliminary results show that it might be possible to publish an obfuscated DNA sequence with a desired level of similarity (utility) to the original DNA sequence. http://arxiv.org/abs/1405.5410
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.
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.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
A survey of heterogeneous information network analysisSOYEON KIM
A Survey of Heterogeneous Information Network Analysis
Chuan Shi, Member, IEEE,
Yitong Li, Jiawei Zhang, Yizhou Sun, Member, IEEE,
and Philip S. Yu, Fellow, IEEE
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2015
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.
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.
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.
Review on Computational Bioinformatics and Molecular Modelling Novel Tool for...ijtsrd
Advancement in science and technology has brought a remarkable change in the field of drug discovery. Earlier it was very difficult to predict the target for receptor but nowadays, it is easy and robust task to dock the target protein with ligand and binding affinity is calculated. Docking helps in the virtual screening of drug along with its hit identification. There are two approaches through which docking can be carried out, shape complementary and stimulation approach. There are many procedures involved in carrying out docking and all require different software's and algorithms. Molecular docking serves as a good platform to screen a large number of ligands and is useful in Drug-DNA studies. This review mainly focuses on the general idea of molecular docking and discusses its major applications, different types of interaction involved and types of docking. Rishabh Jain "Review on Computational Bioinformatics and Molecular Modelling: Novel Tool for Drug Discovery" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-1 , December 2018, URL: http://www.ijtsrd.com/papers/ijtsrd18914.pdf
http://www.ijtsrd.com/pharmacy/pharmacoinformatics/18914/review-on-computational-bioinformatics-and-molecular-modelling-novel-tool-for-drug-discovery/rishabh-jain
Efficiency of LSB steganography on medical information IJECEIAES
The development of the medical field had led to the transformation of communication from paper information into the digital form. Medical information security had become a great concern as the medical field is moving towards the digital world and hence patient information, disease diagnosis and so on are all being stored in the digital image. Therefore, to improve the medical information security, securing of patient information and the increasing requirements for communication to be transferred between patients, client, medical practitioners, and sponsors is essential to be secured. The core aim of this research is to make available a complete knowledge about the research trends on LSB Steganography Technique, which are applied to securing medical information such as text, image, audio, video and graphics and also discuss the efficiency of the LSB technique. The survey findings show that LSB steganography technique is efficient in securing medical information from intruder.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Usin...Kato Mivule
Kato Mivule, Claude Turner, "A Comparative Analysis of Data Privacy and Utility Parameter Adjustment, Using Machine Learning Classification as a Gauge", Procedia Computer Science, Volume 20, 2013, Pages 414-419, Baltimore MD, USA
ONTOLOGY-DRIVEN INFORMATION RETRIEVAL FOR HEALTHCARE INFORMATION SYSTEM : ...IJNSA Journal
In health research, one of the major tasks is to retrieve, and analyze heterogeneous databases containing
one single patient’s information gathered from a large volume of data over a long period of time. The
main objective of this paper is to represent our ontology-based information retrieval approach for
clinical Information System. We have performed a Case Study in the real life hospital settings. The results
obtained illustrate the feasibility of the proposed approach which significantly improved the information
retrieval process on a large volume of data over a long period of time from August 2011 until January
2012
A Codon Frequency Obfuscation Heuristic for Raw Genomic Data PrivacyKato Mivule
Genomic data provides clinical researchers with vast opportunities to study various patient ailments. Yet the same data contains revealing information, some of which a patient might want to remain concealed. The question then arises: how can an entity transact in full DNA data while concealing certain sensitive pieces of information in the genome sequence, and maintain DNA data utility? As a response to this question, we propose a codon frequency obfuscation heuristic, in which a redistribution of codon frequency values with highly expressed genes is done in the same amino acid group, generating an obfuscated DNA sequence. Our preliminary results show that it might be possible to publish an obfuscated DNA sequence with a desired level of similarity (utility) to the original DNA sequence. http://arxiv.org/abs/1405.5410
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.
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.
The NRNB has been funded as an NIGMS Biomedical Technology Research Resource since 2010. During the previous five-year period, NRNB investigators introduced a series of innovative methods for network biology including network-based biomarkers, network-based stratification of genomes, and automated inference of gene ontologies using network data. Over the next five years, we will seek to catalyze major phase transitions in how biological networks are represented and used, working across three broad themes: (1) From static to differential networks, (2) From descriptive to predictive networks, and (3) From flat to hierarchical networks bridging across scales. All of these efforts leverage and further support our growing stable of network technologies, including the popular Cytoscape network analysis infrastructure.
DSAGLSTM-DTA: Prediction of Drug-Target Affinity using Dual Self-Attention an...mlaij
The research on affinity between drugs and targets (DTA) aims to effectively narrow the target search space for drug repurposing. Therefore, reasonable prediction of drug and target affinities can minimize the waste of resources such as human and material resources. In this work, a novel graph-based model called DSAGLSTM-DTA was proposed for DTA prediction. The proposed model is unlike previous graph-based drug-target affinity model, which incorporated self-attention mechanisms in the feature extraction process of drug molecular graphs to fully extract its effective feature representations. The features of each atom in the 2D molecular graph were weighted based on attention score before being aggregated as molecule representation and two distinct pooling architectures, namely centralized and distributed architectures were implemented and compared on benchmark datasets. In addition, in the course of processing protein sequences, inspired by the approach of protein feature extraction in GDGRU-DTA, we continue to interpret protein sequences as time series and extract their features using Bidirectional Long Short-Term Memory (BiLSTM) networks, since the context-dependence of long amino acid sequences. Similarly, DSAGLSTM-DTA also utilized a self-attention mechanism in the process of protein feature extraction to obtain comprehensive representations of proteins, in which the final hidden states for element in the batch were weighted with the each unit output of LSTM, and the results were represented as the final feature of proteins. Eventually, representations of drug and protein were concatenated and fed into prediction block for final prediction. The proposed model was evaluated on different regression datasets and binary classification datasets, and the results demonstrated that DSAGLSTM-DTA was superior to some state-ofthe-art DTA models and exhibited good generalization ability.
DSAGLSTM-DTA: PREDICTION OF DRUG-TARGET AFFINITY USING DUAL SELF-ATTENTION AN...mlaij
The research on affinity between drugs and targets (DTA) aims to effectively narrow the target search
space for drug repurposing. Therefore, reasonable prediction of drug and target affinities can minimize the
waste of resources such as human and material resources. In this work, a novel graph-based model called
DSAGLSTM-DTA was proposed for DTA prediction. The proposed model is unlike previous graph-based
drug-target affinity model, which incorporated self-attention mechanisms in the feature extraction process
of drug molecular graphs to fully extract its effective feature representations. The features of each atom in
the 2D molecular graph were weighted based on attention score before being aggregated as molecule
representation and two distinct pooling architectures, namely centralized and distributed architectures
were implemented and compared on benchmark datasets. In addition, in the course of processing protein
sequences, inspired by the approach of protein feature extraction in GDGRU-DTA, we continue to
interpret protein sequences as time series and extract their features using Bidirectional Long Short-Term
Memory (BiLSTM) networks, since the context-dependence of long amino acid sequences. Similarly,
DSAGLSTM-DTA also utilized a self-attention mechanism in the process of protein feature extraction to
obtain comprehensive representations of proteins, in which the final hidden states for element in the batch
were weighted with the each unit output of LSTM, and the results were represented as the final feature of
proteins. Eventually, representations of drug and protein were concatenated and fed into prediction block
for final prediction. The proposed model was evaluated on different regression datasets and binary
classification datasets, and the results demonstrated that DSAGLSTM-DTA was superior to some state-ofthe-art DTA models and exhibited good generalization ability.
Outcome-guided mutual information networks for investigating gene-gene intera...SOYEON KIM
TBC2014 poster
"Outcome-guided mutual information networks for investigating gene-gene interaction effects on clinical outcomes", Hyun-hwan Jeong, So Yeon Kim, Kyubum Wee, Kyung-Ah Sohn
A novel optimized deep learning method for protein-protein prediction in bioi...IJECEIAES
Proteins have been shown to perform critical activities in cellular processes and are required for the organism's existence and proliferation. On complicated protein-protein interaction (PPI) networks, conventional centrality approaches perform poorly. Machine learning algorithms based on enormous amounts of data do not make use of biological information's temporal and spatial dimensions. As a result, we developed a sequence- dependent PPI prediction model using an Aquila and shark noses-based hybrid prediction technique. This model operates in two stages: feature extraction and prediction. The features are acquired using the semantic similarity technique for good results. The acquired features are utilized to predict the PPI using hybrid deep networks long short-term memory (LSTM) networks and restricted Boltzmann machines (RBMs). The weighting parameters of these neural networks (NNs) were changed using a novel optimization approach hybrid of aquila and shark noses (ASN), and the results revealed that our proposed ASN-based PPI prediction is more accurate and efficient than other existing techniques.
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.
OPTIMIZED PREDICTION IN MEDICAL DIAGNOSIS USING DNA SEQUENCES AND STRUCTURE I...IAEME Publication
The prediction in medical diagnosis is an important role in the field of bio informatics which acts as a vital tool for handling and maintaining human health care in an efficient way. The technologies involves in the process prediction are al complex ones to integrate and implements in an effective way. The optimized prediction in the medical diagnosis reduces the medical treatment complexities along with the time and cost saving benefits. The DNA (Deoxyribonucleic Acid) sequences and structure information will make the disease diagnosis prediction with more accuracy and optimizations for comparisons and conclusions. For the impact of inheritance based diseases the earlier predictions will definitely act as a pivotal process for improving the cure strategies for any human being. The existence case disease diagnosis based prediction will also help and supports the medical treatment in an advanced way both in technical and process wise. This paper proposed Optimized Prediction in Medical Diagnosis Using DNA Sequences and Structure Information with pattern matching and comparison analysis using DNA sequence and structure information for individual patient condition. In future this paper will be extended with artificial intelligence based implementation through genetic algorithm to attain a DNA based Medical Diagnosis Disease Prediction System.
Statistical Analysis based Hypothesis Testing Method in Biological Knowledge ...ijcsa
The correlation and interactions among different biological entities comprise the biological system. Although already revealed interactions contribute to the understanding of different existing systems, researchers face many questions everyday regarding inter-relationships among entities. Their queries have potential role in exploring new relations which may open up a new area of investigation. In this paper, we introduce a text mining based method for answering the biological queries in terms of statistical computation such that researchers can come up with new knowledge discovery. It facilitates user to submit their query in natural linguistic form which can be treated as hypothesis. Our proposed approach analyzes the hypothesis and measures the p-value of the hypothesis with respect to the existing literature. Based on the measured value, the system either accepts or rejects the hypothesis from statistical point of view. Moreover, even it does not find any direct relationship among the entities of the hypothesis, it presents a network to give an integral overview of all the entities through which the entities might be related. This is also congenial for the researchers to widen their view and thus think of new hypothesis for further investigation. It assists researcher to get a quantitative evaluation of their assumptions such that they can reach a logical conclusion and thus aids in relevant re-searches of biological knowledge discovery. The system also provides the researchers a graphical interactive interface to submit their hypothesis for assessment in a more convenient way.
National Resource for Networks Biology's TR&D Theme 1: In this theme, we will develop a series of tools and methodologies for conducting differential analyses of biological networks perturbed under multiple conditions. The novel algorithmic methodologies enable us to make use of high-throughput proteomic level data to recover biological networks under specific biological perturbations. The software tools developed in this project enable researchers to further predict, analyze, and visualize the effects of these perturbations and alterations, while enabling researchers to aggregate additional information regarding the known roles of the involved interactions and their participants.
The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data can be made machine and AI-ready - the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In this talk, I will discuss our work in (1) building an integrative knowledge infrastructure to prepare FAIR and "AI-ready" data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic and latent representations, and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. Our work, and those of others in the field, creates a baseline for building trustworthy and easy to deploy AI models in biomedicine.
Bio
Dr. Michel Dumontier is the Distinguished Professor of Data Science at Maastricht University, founder and executive director of the Institute of Data Science, and co-founder of the FAIR (Findable, Accessible, Interoperable and Reusable) data principles. His research explores socio-technological approaches for responsible discovery science, which includes collaborative multi-modal knowledge graphs, privacy-preserving distributed data mining, and AI methods for drug discovery and personalized medicine. His work is supported through the Dutch National Research Agenda, the Netherlands Organisation for Scientific Research, Horizon Europe, the European Open Science Cloud, the US National Institutes of Health, and a Marie-Curie Innovative Training Network. He is the editor-in-chief for the journal Data Science and is internationally recognized for his contributions in bioinformatics, biomedical informatics, and semantic technologies including ontologies and linked data.
New prediction method for data spreading in social networks based on machine ...TELKOMNIKA JOURNAL
Information diffusion prediction is the study of the path of dissemination of news, information, or topics in a structured data such as a graph. Research in this area is focused on two goals, tracing the information diffusion path and finding the members that determine future the next path. The major problem of traditional approaches in this area is the use of simple probabilistic methods rather than intelligent methods. Recent years have seen growing interest in the use of machine learning algorithms in this field. Recently, deep learning, which is a branch of machine learning, has been increasingly used in the field of information diffusion prediction. This paper presents a machine learning method based on the graph neural network algorithm, which involves the selection of inactive vertices for activation based on the neighboring vertices that are active in a given scientific topic. Basically, in this method, information diffusion paths are predicted through the activation of inactive vertices byactive vertices. The method is tested on three scientific bibliography datasets: The Digital Bibliography and Library Project (DBLP), Pubmed, and Cora. The method attempts to answer the question that who will be the publisher of thenext article in a specific field of science. The comparison of the proposed method with other methods shows 10% and 5% improved precision in DBL Pand Pubmed datasets, respectively.
Similar to Network embedding in biomedical data science (20)
Sequencing is one of the major technological advancement that has taken shape in the last two or three decade. Starting from Sanger and Maxam-Gilbert sequencing methods to the latest high-throughput methods, sequencing technologies has changed the the landscape of biological sciences.
This slide takes a look a the major sequencing methods over time.
Note: Several images included here have been sourced from GOOGLE IMAGES. The content has been extracted from several SCIENTIFIC PAPERS and WEBSITES.
PLEASE DO CONTACT THE AUTHOR DIRECTLY IF ANY COPYRIGHT ISSUE ARISES.
Humans are 99% similar to each other; but it is the 1% that is the cause of concern. This relatively small difference actually how a drug will effect our body. Pharmacogenomics is the study of how genes affect a person’s response to drugs. In order to prevent any unwanted reactions it has become necessary to consider one's genome while prescribing medicine. Thus pharmacogenomics is the starting point of personalized medicine.
Ab Initio Protein Structure Prediction is a method to determine the tertiary structure of protein in the absence of experimentally solved structure of a similar/homologous protein. This method builds protein structure guided by energy function.
I had prepared this presentation for an internal project during my masters degree course.
Deodar has been given the status of State plant in the Indian state of Himachal Pradesh rightly due to its presence in abundance in the state. The tree belongs to the genus cedrus. This presentation tries to explore the different species of cedrus found worldwide, the regions they are found, uses, conservation status etc.
Note: The images used as background in each slide belongs to the author.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Observation of Io’s Resurfacing via Plume Deposition Using Ground-based Adapt...Sérgio Sacani
Since volcanic activity was first discovered on Io from Voyager images in 1979, changes
on Io’s surface have been monitored from both spacecraft and ground-based telescopes.
Here, we present the highest spatial resolution images of Io ever obtained from a groundbased telescope. These images, acquired by the SHARK-VIS instrument on the Large
Binocular Telescope, show evidence of a major resurfacing event on Io’s trailing hemisphere. When compared to the most recent spacecraft images, the SHARK-VIS images
show that a plume deposit from a powerful eruption at Pillan Patera has covered part
of the long-lived Pele plume deposit. Although this type of resurfacing event may be common on Io, few have been detected due to the rarity of spacecraft visits and the previously low spatial resolution available from Earth-based telescopes. The SHARK-VIS instrument ushers in a new era of high resolution imaging of Io’s surface using adaptive
optics at visible wavelengths.
Slide 1: Title Slide
Extrachromosomal Inheritance
Slide 2: Introduction to Extrachromosomal Inheritance
Definition: Extrachromosomal inheritance refers to the transmission of genetic material that is not found within the nucleus.
Key Components: Involves genes located in mitochondria, chloroplasts, and plasmids.
Slide 3: Mitochondrial Inheritance
Mitochondria: Organelles responsible for energy production.
Mitochondrial DNA (mtDNA): Circular DNA molecule found in mitochondria.
Inheritance Pattern: Maternally inherited, meaning it is passed from mothers to all their offspring.
Diseases: Examples include Leber’s hereditary optic neuropathy (LHON) and mitochondrial myopathy.
Slide 4: Chloroplast Inheritance
Chloroplasts: Organelles responsible for photosynthesis in plants.
Chloroplast DNA (cpDNA): Circular DNA molecule found in chloroplasts.
Inheritance Pattern: Often maternally inherited in most plants, but can vary in some species.
Examples: Variegation in plants, where leaf color patterns are determined by chloroplast DNA.
Slide 5: Plasmid Inheritance
Plasmids: Small, circular DNA molecules found in bacteria and some eukaryotes.
Features: Can carry antibiotic resistance genes and can be transferred between cells through processes like conjugation.
Significance: Important in biotechnology for gene cloning and genetic engineering.
Slide 6: Mechanisms of Extrachromosomal Inheritance
Non-Mendelian Patterns: Do not follow Mendel’s laws of inheritance.
Cytoplasmic Segregation: During cell division, organelles like mitochondria and chloroplasts are randomly distributed to daughter cells.
Heteroplasmy: Presence of more than one type of organellar genome within a cell, leading to variation in expression.
Slide 7: Examples of Extrachromosomal Inheritance
Four O’clock Plant (Mirabilis jalapa): Shows variegated leaves due to different cpDNA in leaf cells.
Petite Mutants in Yeast: Result from mutations in mitochondrial DNA affecting respiration.
Slide 8: Importance of Extrachromosomal Inheritance
Evolution: Provides insight into the evolution of eukaryotic cells.
Medicine: Understanding mitochondrial inheritance helps in diagnosing and treating mitochondrial diseases.
Agriculture: Chloroplast inheritance can be used in plant breeding and genetic modification.
Slide 9: Recent Research and Advances
Gene Editing: Techniques like CRISPR-Cas9 are being used to edit mitochondrial and chloroplast DNA.
Therapies: Development of mitochondrial replacement therapy (MRT) for preventing mitochondrial diseases.
Slide 10: Conclusion
Summary: Extrachromosomal inheritance involves the transmission of genetic material outside the nucleus and plays a crucial role in genetics, medicine, and biotechnology.
Future Directions: Continued research and technological advancements hold promise for new treatments and applications.
Slide 11: Questions and Discussion
Invite Audience: Open the floor for any questions or further discussion on the topic.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
1. Network embedding in biomedical data science
Chang Su, Jie Tong, Yongjun Zhu, Peng Cui & Fei Wang
Briefings in Bioinformatics
December 2018
https://doi.org/10.1093/bib/bby117
Journal Club 01-Feb-2019
2. “Recent advances in biomedical research as well as computer software and
hardware technologies have led to an inrush of a large number of relational data
interlinking drugs, genes, proteins, chemical compounds, diseases and medical
concepts extracted from clinical data.”
The availability of such
relational data has
greatly facilitated the
biomedical studies, such
as network biology,
network medicine,
pharmacogenomics,
disease diagnosis,
clinical phenotyping,
etc.
Numerous network-
based learning methods
have been developed to
explore reliable tools for
multiple applications.
Although existing
methods show capacity
of processing networks
and demonstrate great
promises, they usually
suffer from high
computational and space
cost, owning to high
dimensionality and
sparsity of the networks.
3. Efficient analysis of these networks heavily relies on the ways how networks are represented. Often, a
discrete adjacency matrix is used to represent a network, which only captures neighboring relationships
between vertices. Indeed, this simple representation cannot embody more complex, higher-order structure
relationships, such as paths, frequent substructure etc. As a result, such a traditional routine often makes
many network analytic tasks computationally expensive and intractable over largescale networks.
--Zhang, Daokun, et al. "Network representation learning: A survey." IEEE transactions on Big Data (2018).
4. 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.
5. In the network embedding space, the relationships
among the nodes, which were originally represented
by edges or other high-order topological measures in
graphs, is captured by the distances between nodes
in the vector space, and the topological and
structural characteristics of a node are encoded into
its embedding vector.
An example is shown in Fig. After embedding the
karate club network into a twodimensional space,
the similar nodes marked by the same color are
close to each other in the embedding space,
demonstrating that the network structure can be
well modeled in the two-dimensional embedding
space.
Ref.: Cui, Peng, et al. "A survey on network embedding." IEEE Transactions on Knowledge and Data Engineering (2018).
Basic idea: Embed nodes so that distances in embedding space reflect node similarities in the original network.
6. A non-attributed network is also known
as homogeneous network, of which all
nodes and edges belong to a unique
type, respectively.
The attributed networks, also known as
heterogeneous networks, allow nodes
and/or edges to belong to multiple
types.
9. Applications in biomedical data science
Table: Biomedical applications of network embedding surveyed in this study.
The use of network embedding for biomedical data analysis is recent and not thoroughly explored.
10. Drug repositioning: Computational drug repositioning, also known as drug repurposing, is a promising and
efficient tool for exploring new usage for existing drugs to save drug development cost and increase
productivity. Drugs bind with target proteins and affect their downstream activity, consequently lead to
impact on human body to treat the disease. A drug repositioning tool usually aims at predicting unknown
drug–target or drug–disease interactions.
11. A network integration approach for drug-target interaction prediction and computational drug repositioning
from heterogeneous information.
-- Luo, Yunan, et al. Nature communications 8.1 (2017): 573.
• Proposed DTINet by extending diffusion component analysis (DCA) by separately performing
random walk with restart (RWR) on drug–drug, drug–disease, drug side effect and drug similarity
networks for drug embedding and on protein–protein, protein–disease and protein similarity
networks for target protein embedding. After that, DTINet projected drugs into the embedding
space of target proteins and made prediction based on geometric proximity.
• Developed a computational pipeline, called DTINet, to predict novel drug–target interactions from
a constructed heterogeneous network, which integrates diverse drug-related information.
• DTINet focuses on learning a low-dimensional vector representation of features, which accurately
explains the topological properties of individual nodes in the heterogeneous network, and then
makes prediction based on these representations via a vector space projection scheme.
• DTINet achieves substantial performance improvement over other state-of-the-art methods for
drug–target interaction prediction.
12. Large‐scale extraction of drug–disease pairs from the medical literature.
-- Wang, Pengwei, et al. Journal of the Association for Information Science and Technology 68.11 (2017): 2649-2661.
• Proposed to detect unknown drug–disease interactions from the medical literature by using
neuro-linguistic programming (NLP) and network embedding techniques.
• Using treatment and inducement drug–disease pairs extracted from 27 million PubMed articles,
they first constructed a heterogeneous network.
• They next expanded the network embedding method, LINE, by modifying the 1st-order proximity
to encode treatment and inducement relations as positive and negative effects to the objective
function, respectively.
• The result showed that the embeddings lead to significant improvement in predictions of both
types of drug–disease interactions.
13. Adverse drug reaction analysis: An adverse drug reaction (ADR) is defined as any undesirable effect from
the medical use of drugs beyond its anticipated therapeutic effects that occurs at a usual dosage. The study
of ADRs is the concern of drug development especially before a drug is launched on clinical application.
Detecting potential ADRs is always time consuming and expensive. To address this, computational methods
based on network embedding have been introduced to ADR analysis.
14. Large-scale structural and textual similarity-based mining of knowledge graph to predict drug–drug interactions.
-- Abdelaziz, Ibrahim, et al. Web Semantics: Science, Services and Agents on the World Wide Web 44 (2017): 104-117.
• Tiresias, a large-scale similarity-based framework that predicts DDIs through link prediction.
• Tiresias takes in various sources of drug-related data and knowledge as inputs, and provides DDI
predictions as outputs.
• The process starts with semantic integration of the input data that results in a knowledge graph
describing drug attributes and relationships with various related entities such as enzymes,
chemical structures, and pathways.
• The knowledge graph is then used to compute several similarity measures between all the drugs
in a scalable and distributed framework.
• The resulting similarity metrics are used to build features for a large-scale logistic regression
model to predict potential DDIs.
15. Exploiting ontology graph for predicting sparsely annotated gene function.
-- Wang, Sheng, et al. Bioinformatics 31.12 (2015): i357-i364.
• Proposed clusDCA to predict gene function, by applying diffusion component analysis (DCA) to
gene–gene interaction and GO to learn low-dimensional representations for genes and GO labels,
respectively.
• Based on such embedding results, they trained a projection model from gene space to GO space
such that genes geometrically closed to their known GO labels.
• It bridges latent gene features and GO labels and results in desirable prediction of sparsely
annotated gene functions.
Genomics Data Analysis
16. Geometric de-noising of protein-protein interaction networks.
-- Kuchaiev, Oleksii, et al. PLoS computational biology 5.8 (2009): e1000454.
• Proposed an embedding algorithm based on multidimensional scaling (MDS) for PPI network de-
noising.
• By using the embeddings of proteins, they predicted new PPIs and assessed the confidence of
existing PPIs.
Proteomics Data Analysis
17. miRNA-Disease Association Prediction with Collaborative Matrix Factorization.
-- Shen, Zhen, et al. Complexity 2017 (2017).
• Developed CMFMDA that introduced matrix factorization to bipartite miRNA-disease network for
embedding to predict new associations.
• In CMFMDA, miRNA functional similarity and disease semantic similarity were involved in
factorization in terms of regularizations to improve embedding.
• The evaluation was performed to discover esophageal neoplasms-related miRNAs that were
previously confirmed by miR2Disease and dbDEMC.
• The results showed that CMFMDA can outperform other computational methods.
Transcriptomics Data Analysis
18. Challenges
Data quality: Unlike other domain where the data are clean and well structured,
networks constructed from the biomedical data are usually noisy and incomplete.
Local and global:
• Performances of the network embedding model and its downstream tasks rely on the type
of structural property to preserve.
• Preserving local property will gather connected nodes in the embedding space, while
preserving global property will project topologically similar (even far separated) nodes
together.
• Designing embedding method by properly considering local and global structure properties
according to application scenarios is an important aspect that will require the development
of novel solutions.
19. Challenges
Network evolution:
• Networks are always not static, especially in the biomedical domain.
• Existing network embedding models mainly focused on the static networks, and the
settings of network evolution were overlooked.
• To learn embeddings for a dynamic network, existing methods should be trained
repeatedly for each timestamp, which is definitely time consuming and may not capture
the temporal properties.
• Therefore, most of the existing network embedding methods cannot be directly applied to
evolving biomedical networks.
20. Challenges
Domain complexity:
• Different from network embedding application in other domains, the issue on biomedicine
and health care is much more complicated.
• For example, in a biomedical network, each interaction between entities usually represents
a complex genetic, pathological or pharmacological event or process, and there is usually
no complete knowledge on how it progresses.
• When applying an embedding model, the biomedical domain knowledge is also needed to
better understand the network structure.
21. Opportunities
Local and global trade-off embedding
Dynamic embedding
Text-associated embedding
Domain-knowledge-associated embedding