Gene Selection Approaches for Classifying Disease Relevant Data Sample
Keywords:Microarrays, gene-expression, genomics, wrapper, dimensionality reduction.
In the latest field of gene expression profiling, the identification of most highly expressed genes with respect to diseases is been in focus lately, As to study the disease types and classify normal from disease syndrome samples. This paper portrays four gene selection approaches such as Pearson correlation, Signal to Noise Correlation, Feature Assessment by Sliding threshold and Feature Assessment by Information Retrieval for retrieving highly relevant genes oriented to a specific disease. This experiment uses various disease dataset for operating on the typical gene selection methods and to select top ten most relevant genes and thus selected genes are learned on using classifiers such as Support Vector Machine, K-Nearest Neighbour and NaÃ¯ve Bayes to classify the specific disease oriented classes distinctively. Here we also compare the performance of our classifier with the previous papers techniques using classification Accuracy.
 Fang OH, Mustapha N & Nasir Sulaiman MD, â€œIntegrating Biological Information for Feature Selection in Microarray Data Classificationâ€, IEEE Computer Society, IEEE Conference on Computer Engineering and Applications, Vol.2, (2010), pp.330-334.
 Osareh A & Shadgar B, â€œMicroarray Data Analysis for Cancer Classificationâ€, IEEE Conference on Computer Engineering and Applications, (2010), pp.125-132.
 Hastie T, Tibshirani R, Eisen MB, Alizadeh A, Levy R, Staudt L & Brown P, â€œGene shaving' as a method for identifying distinct sets of genes with similar expression patternsâ€, Genome biology, Vol.1, No.2, (2000).
 Saeys Y, Inza I & Larranaga P, â€œReview feature selection technique bioinformaticsâ€, Bioinformatics, Vol.23, No.19, (2007), pp.2507-2517.
 Maji P & Pal SK, â€œFuzzy Rough sets for information measures and selection of relevant genes from microarray dataâ€, IEEE Transaction on Systems, Man, and Cybernetics, Vol.40, No.3, (2010), pp.741-752.
 Jose CHH, BÂ´eatrice D & Jin KH, â€œA Genetic Embedded Approach for Gene Selection and Classification of Microarray Dataâ€, Springer, (2007), pp.90-101.
 Wasikowski M & Chen X, â€œCombating the small class imbalance problem using feature selectionâ€, IEEE Trans. Knowledge and Data Engineering, Vol.22, No.10, (2010), pp.1388-1400.
 Davis J & Goadrich M, â€œThe Relationship between Precision-Recall and ROC Curvesâ€, 23rd Intâ€™l Conf. Machine Learning, (2006), pp.30-38.
 Chen X & Wasikowski, â€œFAST: A ROC-Based Feature Selection Metric for Small Samples and Imbalanced Data Classification Problemsâ€, Proc. ACM SIGKDD, (2008), pp.124-133.
 Ganeshkumar P, Aruldoss T, Devaraj D & Renukadevi M, â€œDesign of fuzzy Expert system for microarray data classification using a novel Genetic Swarm Algorithmâ€, Expert Systems with Applications, Vol.39, (2012), pp.1811-1821.
 Maji P, â€œFuzzyâ€“Rough Supervised Attribute Clustering Algorithm and Classification of Microarray Dataâ€, IEEE Transaction on Systems, Man and Cybernetics, (2010), pp.1-10.
 Golub T, Slonim D, Tamayo P, Huard C, Gaasenbeek M, Mesirov J, Coller H, Loh M, Dowing J, Caligiuri M, Bloomfield C & Lander E, â€œMolecular classification of cancer: Class discovery and class prediction by gene expression monitoringâ€, Science, Vol.286, (1999), pp.531-537.
 Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, Mack D & Levine A.J, â€œBroad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arraysâ€, Proc.Nat. Acad. Sci. U.S.A., Vol.96, No.12, (1999), pp.6745-6750.
 Welsh JB, Sapinoso LM, Su AI, Kern SG, Wang-Rodriguez J & Moskaluk CA, â€œAnalysis of gene expression identifies candidate markers and pharmacological targets in prostate Cancerâ€, Cancer Research, Vol.61, (2001), pp.5974â€“5978.
 Hayward J, Alvarez SA, Ruiz C, Sullivan M, Tseng J & Whalen G, â€œMachine learning of clinical performance in pancreatic cancer databaseâ€, Artificial Intelligence in Medicine, Vol.49, No.3, (2010), pp.187-193.
 Kraan TCTM, Gaalen VFA, Kasperkovitz PV, Verbeet NL, Smeets TJM, Kraan MC, Fero M, Tak PP, Huizinga TWJ, Pieterman E, Breedveld FC, Breedveld AA, Alizadech AA & Verweij CL, â€œRheumatoid arthritis is a heterogenous disease: Evidence for differences in activation of STAT-1 pathway between rheumatoid tissuesâ€, Arthritis Rheum., Vol.48, No.8, (2003), pp.2312-2145.
 Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A & Powell JI, â€œDistinct types of diffuse large B-cell lymphoma identified by gene expression profilingâ€, Nature, Vol.403, No.6769, (2000), pp.503-511.
 Teixeira VH, Olaso R, Martin-Magniette ML, Lasbleiz S, Jacq L, Oliveira CR & Petit-Teixeira E, â€œTranscriptome analysis describing new immunity and defense genes in peripheral blood mononuclear cells of rheumatoid arthritis patientsâ€, PloS one, Vol.4, No.8, (2009), pp.e6803.
 Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J and Houstis, N, â€œPGC-1Î±-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetesâ€, Nature genetics, Vol.34, No.3, (2003), pp.267-273.
 National Centre for Biotechnology Information (NCBI), U.S. National Library of Medicine, Available Online at http://www.ncbi.nlm.nih.gov, 2009.
 Hayward J, Alvarez SA, Ruiz C, Sullivan M, Tseng J & Whalen G, â€œKnowledge discovery in clinical performance of cancer patientsâ€, IEEE International conference on Bio-Informatics and Bio-Medicine, Vol.49, No.3, (2010), pp.187-193.
 Villalobos AntÃºnez, JV (2017). Karl R. Popper, HerÃ¡clito y la invenciÃ³n del logos. Un contexto para la FilosofÃa de las Ciencias Sociales. OpciÃ³n Vol. 33, NÃºm. 84. 5-11