Perturbation and candidate analysis to combat overfitting of gene expression microarray data. (3rd January 2012)

• Record Type: Journal Article
• Authors: Mathur, Ravi; Schaffer, J. David; Jr., Walker H. Land; Heine, John J.; Hernandez, Jonathan M.; Yeatman, Timothy
• Is Part Of: International journal of computational biology and drug design. Volume 4:Number 4(2011)
• Page Start: 307
• Page End: 315
• Publication Date: 2012-01-03
• Subjects: Az value -- colorectal cancer -- cross–validation -- DNA microarray -- GAs -- genetic algorithms -- noise perturbation -- overfitting -- ROC curve -- support vector machines -- SVM -- gene expression microarray data; Computational biology -- Periodicals; Drugs -- Design -- Periodicals; 570.285
• Languages: English
• ISSNs: 1756-0756

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.