title: Classification and feature selection algorithms for multi-class CGH data.
abstract: Recurrent chromosomal alterations provide cytological and molecular positions for the diagnosis and prognosis of cancer. Comparative genomic hybridization (CGH) has been useful in understanding these alterations in cancerous cells. CGH datasets consist of samples that are represented by large dimensional arrays of intervals. Each sample consists of long runs of intervals with losses and gains. In this article, we develop novel SVM-based methods for classification and feature selection of CGH data. For classification, we developed a novel similarity kernel that is shown to be more effective than the standard linear kernel used in SVM. For feature selection, we propose a novel method based on the new kernel that iteratively selects features that provides the maximum benefit for classification. We compared our methods against the best wrapper-based and filter-based approaches that have been used for feature selection of large dimensional biological data. Our results on datasets generated from the Progenetix database, suggests that our methods are considerably superior to existing methods.
  
  
  BACKGROUND
  All software developed in this article can be downloaded from http://plaza.ufl.edu/junliu/feature.tar.gz.
authors.affiliation: Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA. juliu@cise.ufl.edu
authors.forename: Jun
authors.initials: J
authors.lastname: Liu
authors.forename: Sanjay
authors.initials: S
authors.lastname: Ranka
authors.forename: Tamer
authors.initials: T
authors.lastname: Kahveci
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods*; D018628:Gene Dosage / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*