title: Perturbation and candidate analysis to combat overfitting of gene expression microarray data.
abstract: Analysis of gene expression microarray datasets presents the high risk of over-fitting (spurious patterns) because of their feature-rich but case-poor nature. This paper describes our ongoing efforts to develop a method to combat over-fitting and determine the strongest signal in the dataset. A GA-SVM hybrid along with Gaussian noise (manual noise gain) is used to discover feature sets of minimal size that accurately classifies the cases under cross-validation. Initial results on a colorectal cancer dataset shows that the strongest signal (modest number of candidates) can be found by a binary search.
authors.affiliation: Department of Bioengineering, Binghamton University, Binghamton, NY 13902, USA.
authors.forename: Ravi
authors.initials: R
authors.lastname: Mathur
authors.forename: J David
authors.initials: JD
authors.lastname: Schaffer
authors.forename: Walker H
authors.initials: WH
authors.lastname: Land
authors.forename: John J
authors.initials: JJ
authors.lastname: Heine
authors.forename: Jonathan M
authors.initials: JM
authors.lastname: Hernandez
authors.forename: Timothy
authors.initials: T
authors.lastname: Yeatman
mesh_terms: D000465:Algorithms; D015179:Colorectal Neoplasms / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D060388:Support Vector Machine