TitleImproved gene selection for classification of microarrays.
Publication TypeJournal Article
Year of Publication2003
AuthorsJaeger J, Sengupta R, Ruzzo WL
JournalPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Date or Month Published2003
KeywordsAlgorithms, Cluster Analysis, Databases, Genetic, Gene Expression Profiling, Humans, Neoplasms, Oligonucleotide Array Sequence Analysis
AbstractIn this paper we derive a method for evaluating and improving techniques for selecting informative genes from microarray data. Genes of interest are typically selected by ranking genes according to a test-statistic and then choosing the top k genes. A problem with this approach is that many of these genes are highly correlated. For classification purposes it would be ideal to have distinct but still highly informative genes. We propose three different pre-filter methods--two based on clustering and one based on correlation--to retrieve groups of similar genes. For these groups we apply a test-statistic to finally select genes of interest. We show that this filtered set of genes can be used to significantly improve existing classifiers.
Alternate JournalPac Symp Biocomput
Citation Key1888
PubMed ID12603017