Analysis of Microarray Data using Artificial Intelligence Based Techniques

Microarray is one of the essential technologies used by the biologist to measure genome-wide expression levels of genes in a particular organism under some particular conditions or stimuli. As microarrays technologies have become more prevalent, the challenges of analyzing these data for getting better insight about biological processes have essentially increased. Due to availability of artificial intelligence based sophisticated computational techniques, such as artificial neural networks, fuzzy logic, genetic algorithms, and many other nature-inspired algorithms, it is possible to analyse microarray gene expression data in more better way. Here, we reviewed artificial intelligence based techniques for the analysis of microarray gene expression data. Further, challenges in the field and future work direction have also been suggested.

[1]  Khalid Raza,et al.  A Novel Anticlustering Filtering Algorithm for the Prediction of Genes as a Drug Target , 2012, ArXiv.

[2]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[3]  Ahsan Raja Chowdhury,et al.  An improved method to infer Gene Regulatory Network using S-System , 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

[4]  Ian B. Jeffery,et al.  Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data , 2006, BMC Bioinformatics.

[5]  Alvis Brazma,et al.  Current approaches to gene regulatory network modelling , 2007, BMC Bioinformatics.

[6]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[7]  Xiaobo Zhou,et al.  A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks , 2004, Bioinform..

[8]  J. Vohradský Neural network model of gene expression , 2001, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[9]  Barnali Sahu,et al.  A Novel Feature Selection Algorithm using Particle Swarm Optimization for Cancer Microarray Data , 2012 .

[10]  Khalid Raza Reconstruction, Topological and Gene Ontology Enrichment Analysis of Cancerous Gene Regulatory Network Modules , 2016 .

[11]  Satoru Miyano,et al.  Identification of Genetic Networks from a Small Number of Gene Expression Patterns Under the Boolean Network Model , 1998, Pacific Symposium on Biocomputing.

[12]  K. Premalatha,et al.  A Comparative Analysis of Swarm Intelligence Techniques for Feature Selection in Cancer Classification , 2014, TheScientificWorldJournal.

[13]  Sung Hoon Jung,et al.  Reconstruction of Gene Regulatory Networks by Neuro-fuzzy Inference Systems , 2007, 2007 Frontiers in the Convergence of Bioscience and Information Technologies.

[14]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[15]  Khalid Raza,et al.  Reconstruction and Analysis of Cancer-specific Gene Regulatory Networks from Gene Expression Profiles , 2013, ArXiv.

[16]  Harish Sharma,et al.  Spider Monkey Optimization algorithm for numerical optimization , 2014, Memetic Computing.

[17]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[18]  Sushmita Mitra,et al.  Genetic Networks and Soft Computing , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[19]  H. Kitano Systems Biology: A Brief Overview , 2002, Science.

[20]  Suteaki Shioya,et al.  Clustering gene expression pattern and extracting relationship in gene network based on artificial neural networks. , 2003, Journal of bioscience and bioengineering.

[21]  A. M. Natarajan,et al.  Comparative Study on Swarm Intelligence Techniques for Biclustering of Microarray Gene Expression Data , 2014 .

[22]  Fei Wang,et al.  A New Approach Combined Fuzzy Clustering and Bayesian Networks for Modeling Gene Regulatory Networks , 2008, 2008 International Conference on BioMedical Engineering and Informatics.

[23]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[24]  Khalid Raza,et al.  Reconstruction and Analysis of Cancerspecific Gene Regulatory Networks from Gene Expression Profiles , 2013 .

[25]  Jinde Cao,et al.  A New Approach to Dynamic Fuzzy Modeling of Genetic Regulatory Networks , 2010, IEEE Transactions on NanoBioscience.

[26]  Khalid Raza,et al.  Clustering analysis of cancerous microarray data , 2014 .

[27]  Sorin Drăghici,et al.  Data Analysis Tools for DNA Microarrays , 2003 .

[28]  Reza Monsefi,et al.  Genetic Regulatory Network Inference using Recurrent Neural Networks trained by a Multi Agent System , 2011 .

[29]  Ujjwal Maulik Analysis of gene microarray data in a soft computing framework , 2011, Appl. Soft Comput..

[30]  Chad Creighton,et al.  Mining gene expression databases for association rules , 2003, Bioinform..

[31]  Charles Wang,et al.  Probability fold change: A robust computational approach for identifying differentially expressed gene lists , 2009, Comput. Methods Programs Biomed..

[32]  Kyriakos Kentzoglanakis,et al.  A Swarm Intelligence Framework for Reconstructing Gene Networks: Searching for Biologically Plausible Architectures , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[33]  Andrei Dragomir,et al.  Gene regulatory networks modelling using a dynamic evolutionary hybrid , 2010, BMC Bioinformatics.

[34]  Hitoshi Iba,et al.  Reconstruction of Gene Regulatory Networks from Gene Expression Data Using Decoupled Recurrent Neural Network Model , 2013 .

[35]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[36]  P. Goodfellow,et al.  DNA microarrays in drug discovery and development , 1999, Nature Genetics.

[37]  Wei Pan,et al.  A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments , 2002, Bioinform..

[38]  Edward R. Dougherty,et al.  Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks , 2002, Bioinform..

[39]  Mohamed O. Elasri,et al.  Microarray Data Clustering Using Particle Swarm Optimization K-means Algorithm , 2005 .

[40]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[41]  Wei-Ning Yang,et al.  Constructing gene regulatory networks from microarray data using GA/PSO with DTW , 2012, Appl. Soft Comput..

[42]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[43]  Mauro Birattari,et al.  Swarm Intelligence , 2012, Lecture Notes in Computer Science.

[44]  Lotfi A. Zadeh,et al.  Fuzzy logic = computing with words , 1996, IEEE Trans. Fuzzy Syst..

[45]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[46]  Xingming Zhao,et al.  Computational Systems Biology , 2013, TheScientificWorldJournal.

[47]  P. Woolf,et al.  A fuzzy logic approach to analyzing gene expression data. , 2000, Physiological genomics.

[48]  Isabel M. Tienda-Luna,et al.  Reverse engineering gene regulatory networks , 2009, IEEE Signal Processing Magazine.

[49]  Khalid Raza,et al.  Evolutionary algorithms in genetic regulatory networks model , 2012, ArXiv.

[50]  S. Bandyopadhyay,et al.  Evolutionary computation in bioinformatics: a review , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[51]  Ting Chen,et al.  Modeling Gene Expression with Differential Equations , 1998, Pacific Symposium on Biocomputing.

[52]  Khalid Raza,et al.  Ant colony optimization for inferring key gene interactions , 2014, 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom).

[53]  E. Dougherty,et al.  Multivariate measurement of gene expression relationships. , 2000, Genomics.

[54]  A. Brazma,et al.  Gene expression data analysis. , 2001, FEBS letters.

[55]  Alina Sîrbu,et al.  Comparison of evolutionary algorithms in gene regulatory network model inference , 2010, BMC Bioinformatics.

[56]  Dervis Karaboga,et al.  AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION , 2005 .

[57]  S Fuhrman,et al.  Reveal, a general reverse engineering algorithm for inference of genetic network architectures. , 1998, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[58]  Werner Dubitzky,et al.  Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks , 2010, BMC Bioinformatics.

[59]  M. Page,et al.  Search for Steady States of Piecewise-Linear Differential Equation Models of Genetic Regulatory Networks , 2008, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[60]  Wei-Po Lee,et al.  A clustering-based approach for inferring recurrent neural networks as gene regulatory networks , 2008, Neurocomputing.

[61]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[62]  Denis Thieffry,et al.  From Logical Regulatory Graphs to Standard Petri Nets: Dynamical Roles and Functionality of Feedback Circuits , 2006, Trans. Comp. Sys. Biology.

[63]  Jean-Loup Faulon,et al.  Boolean dynamics of genetic regulatory networks inferred from microarray time series data , 2007, Bioinform..

[64]  Monika Heiner,et al.  STEPP - Search Tool for Exploration of Petri net Paths: A new tool for Petri net-based path analysis in biochemical networks , 2004, Silico Biol..

[65]  Gary D. Stormo,et al.  Modeling Regulatory Networks with Weight Matrices , 1998, Pacific Symposium on Biocomputing.

[66]  Richard Simon,et al.  A random variance model for detection of differential gene expression in small microarray experiments , 2003, Bioinform..

[67]  Li-Yeh Chuang,et al.  Tabu Search and Binary Particle Swarm Optimization for Feature Selection Using Microarray Data , 2009, J. Comput. Biol..

[68]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[69]  Khalid Raza,et al.  Soft Computing Approach for Modeling Genetic Regulatory Networks , 2012, ACITY.

[70]  M. Eisen,et al.  Gene expression informatics —it's all in your mine , 1999, Nature Genetics.

[71]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[72]  Fuzzy Logic = Computing with Words - Fuzzy Systems, IEEE Transactions on , 2009 .

[73]  James M. Keller,et al.  Applications of Fuzzy Logic in Bioinformatics , 2008, Series on Advances in Bioinformatics and Computational Biology.

[74]  Wang Yuan-yuan,et al.  Particle Swarm Optimization Algorithm , 2009 .

[75]  S. Dudoit,et al.  STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS , 2002 .

[76]  Amit Konar,et al.  A recurrent fuzzy neural model of a gene regulatory network for knowledge extraction using differential evolution , 2009, 2009 IEEE Congress on Evolutionary Computation.

[77]  Rainer Breitling,et al.  Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments , 2004, FEBS letters.

[78]  Atul Butte,et al.  The use and analysis of microarray data , 2002, Nature Reviews Drug Discovery.

[79]  Donald C. Wunsch,et al.  Modeling of gene regulatory networks with hybrid differential evolution and particle swarm optimization , 2007, Neural Networks.

[80]  Guy Karlebach,et al.  Modelling and analysis of gene regulatory networks , 2008, Nature Reviews Molecular Cell Biology.

[81]  Wei-Chung Cheng,et al.  Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm , 2014, BMC Bioinformatics.

[82]  Carlos Gershenson,et al.  Artificial Neural Networks for Beginners , 2003, ArXiv.

[83]  Robert Reynolds,et al.  Fuzzy logic-based gene regulatory network , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[84]  E. Keedwell,et al.  Modelling gene regulatory data using artificial neural networks , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[85]  L. Hood Systems biology: integrating technology, biology, and computation , 2003, Mechanisms of Ageing and Development.

[86]  H. Iba,et al.  Reverse engineering genetic networks using evolutionary computation. , 2005, Genome informatics. International Conference on Genome Informatics.

[87]  J. Tyson,et al.  The dynamics of cell cycle regulation. , 2002, BioEssays : news and reviews in molecular, cellular and developmental biology.

[88]  Robert Clarke,et al.  Reverse engineering module networks by PSO-RNN hybrid modeling , 2009, BMC Genomics.

[89]  Janez Brest,et al.  A Brief Review of Nature-Inspired Algorithms for Optimization , 2013, ArXiv.

[90]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[91]  Tianhai Tian,et al.  Stochastic neural network models for gene regulatory networks , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[92]  Trevor I. Dix,et al.  Fuzzy Model for Gene Regulatory Network , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[93]  De-Shuang Huang,et al.  The analysis of microarray datasets using a genetic programming , 2009, 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[94]  R. Tibshirani,et al.  Significance analysis of microarrays applied to the ionizing radiation response , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[95]  Gordon K Smyth,et al.  Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments , 2004, Statistical applications in genetics and molecular biology.

[96]  De-Pei Liu,et al.  Charting gene regulatory networks: strategies, challenges and perspectives. , 2004, The Biochemical journal.

[97]  Khalid Raza Formal concept analysis for knowledge discovery from biological data , 2017, Int. J. Data Min. Bioinform..

[98]  Jung-Hsien Chiang,et al.  Modeling human cancer-related regulatory modules by GA-RNN hybrid algorithms , 2007, BMC Bioinformatics.

[99]  Doulaye Dembélé,et al.  Fuzzy C-means Method for Clustering Microarray Data , 2003, Bioinform..

[100]  Khalid Raza,et al.  A Comprehensive Evaluation of Machine Learning Techniques for Cancer Class Prediction Based on Microarray Data , 2013, Int. J. Bioinform. Res. Appl..

[101]  Chunguang Zhou,et al.  Combination of neuro-fuzzy network models with biological knowledge for reconstructing gene regulatory networks , 2011 .