title: Comparison of feature selection and classification combinations for cancer classification using microarray data.
abstract: High throughput gene expression data can be used to identify biomarker profiles for classification. The accuracy of microarray based sample classification depends on the algorithm employed for selecting the features (genes) used for classification, and the classification algorithm. We have evaluated the performance of over 2000 combinations of feature selection and classification algorithms in classifying cancer datasets. One of these combinations (SVM for ranking genes + SMO) shows excellent classification accuracy using a small number of genes across three cancer datasets tested. Notably, classification using 15 selected genes yields 96% accuracy for a dataset obtained on an independent microarray platform.
authors.affiliation: Department of Bioinformatics, Dr. D.Y. Patil Biotechnology and Bioinformatics Institute, Akurdi, Pune 411044, India. vini_vij86@yahoo.co.in
authors.forename: Vijayan
authors.initials: V
authors.lastname: Vinaya
authors.forename: Nadeem
authors.initials: N
authors.lastname: Bulsara
authors.forename: Chetan J
authors.initials: CJ
authors.lastname: Gadgil
authors.forename: Mugdha
authors.initials: M
authors.lastname: Gadgil
mesh_terms: D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D009369:Neoplasms / Q000145:classification* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*