暂无分享,去创建一个
[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 .