Novel classification scheme for temporal genomic and proteomic problems

For over a decade genomic and proteomic datasets present a challenge for various statistical and machine learning methods. Most of microarray or mass spectrometry based datasets consist of a small number of samples with a large number of gene or protein expression measurements, but in the past few years new types of datasets with an additional time component are becoming available. This type of datasets offer new opportunities for development of new classification and gene selection techniques where one of the problems is the reduction of high-dimensionality. This paper presents a novel classification technique which combines feature extraction and feature selection to obtain the optimal set of genes available to a classifier. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.