A Survey on Data Mining Of Gene ExpressionData for Gene Function Prediction

Mining the gene expression data for predicting the gene functioning for the possibility of cancerous behavior and utilizing the same in prompt and precise diagnosis. This paper presents detail survey of existing approaches and methods used for mining gene expression. This paper also summarizes and tests the viability of different methods that can be used for mining the gene expression data.

[1]  Aidong Zhang,et al.  Advanced Analysis of Gene Expression Microarray Data , 2006, Science, Engineering, and Biology Informatics.

[2]  Mohamed A. Ismail,et al.  αCORR: a novel algorithm for clustering gene expression data , 2007, 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering.

[3]  Yanqing Zhang,et al.  Recursive Fuzzy Granulation for Gene Subsets Extraction and Cancer Classification , 2008, IEEE Transactions on Information Technology in Biomedicine.

[4]  Stephen G. MacDonell,et al.  Combining techniques to optimize effort predictions in software project management , 2003, J. Syst. Softw..

[5]  Kenneth A. De Jong,et al.  An Evolutionary Algorithm Approach for Feature Generation from Sequence Data and Its Application to DNA Splice Site Prediction , 2012, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[6]  Aidong Zhang,et al.  Cluster analysis for gene expression data: a survey , 2004, IEEE Transactions on Knowledge and Data Engineering.

[7]  Søren Brunak,et al.  Using Electronic Patient Records to Discover Disease Correlations and Stratify Patient Cohorts , 2011, PLoS Comput. Biol..

[8]  A. Adly,et al.  Bio-inspired algorithm for classification association rules , 2012, 2012 8th International Conference on Informatics and Systems (INFOS).

[9]  Michael A. Cusumano,et al.  Software Development Worldwide: The State of the Practice , 2003, IEEE Softw..

[10]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[11]  Francisco Azuaje,et al.  Discovering relevance knowledge in data: a growing cell structures approach , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[12]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[13]  C. Lee Giles,et al.  Self-Organization and Identification of Web Communities , 2002, Computer.

[14]  S. R,et al.  Data Mining with Big Data , 2017, 2017 11th International Conference on Intelligent Systems and Control (ISCO).

[15]  Xiaohua Hu,et al.  Data Mining and Predictive Modeling of Biomolecular Network from Biomedical Literature Databases , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[16]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[17]  Rency S Varghese,et al.  Increasing the efficiency of fuzzy logic-based gene expression data analysis. , 2003, Physiological genomics.

[18]  Iñaki Inza,et al.  Approaching Sentiment Analysis by using semi-supervised learning of multi-dimensional classifiers , 2012, Neurocomputing.

[19]  Bing Liu,et al.  Sentiment Analysis and Subjectivity , 2010, Handbook of Natural Language Processing.

[20]  Y. Tu,et al.  Quantitative noise analysis for gene expression microarray experiments , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[21]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[22]  Ebroul Izquierdo,et al.  A Biologically Inspired System for Classification of Natural Images , 2007, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Søren Brunak,et al.  Compass: A hybrid method for clinical and biobank data mining , 2014, J. Biomed. Informatics.

[24]  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.

[25]  Werner Dubitzky,et al.  A Practical Approach to Microarray Data Analysis , 2003, Springer US.

[26]  Xiaobo Zhou,et al.  Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor design , 2003, Signal Process..

[27]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[28]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[29]  Jian Gong,et al.  Modeling gene expression networks using fuzzy logic , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[30]  T. Marwala,et al.  A Hybrid Fuzzy-SVM classifier, applied to gene expression profiling for automated leukaemia diagnosis , 2008, 2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel.

[31]  Li Xiu,et al.  Application of data mining techniques in customer relationship management: A literature review and classification , 2009, Expert Syst. Appl..

[32]  Chin-Teng Lin,et al.  A recurrent self-organizing neural fuzzy inference network , 1999, IEEE Trans. Neural Networks.

[33]  Keith C. C. Chan,et al.  Inferring Gene Regulatory Networks From Expression Data by Discovering Fuzzy Dependency Relationships , 2008, IEEE Transactions on Fuzzy Systems.

[34]  James M. Bower,et al.  Computational modeling of genetic and biochemical networks , 2001 .

[35]  Yong Xu,et al.  Neuro-Fuzzy Ensemble Approach for Microarray Cancer Gene Expression Data Analysis , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

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

[37]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy sets: past, present and future , 2003, EUSFLAT Conf..

[38]  Pratima Gautam,et al.  A Model for Mining Multilevel Fuzzy Association Rule in Database , 2010, ArXiv.

[39]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[40]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .