title: Microarray-based cancer diagnosis with artificial neural networks.
abstract: In recent years, the advent of experimental methods to probe gene expression profiles of cancer on a genome-wide scale has led to widespread use of supervised machine learning algorithms to characterize these profiles. The main applications of these analysis methods range from assigning functional classes of previously uncharacterized genes to classification and prediction of different cancer tissues. This article surveys the application of machine learning algorithms to classification and diagnosis of cancer based on expression profiles. To exemplify the important issues of the classification procedure, the emphasis of this article is on one such method, namely artificial neural networks. In addition, methods to extract genes that are important for the performance of a classifier, as well as the influence of sample selection on prediction results are discussed.
authors.affiliation: Complex Systems Division, Department of Theoretical Physics, Lund University, Sweden.
authors.forename: Markus
authors.initials: M
authors.lastname: Ringnér
authors.forename: Carsten
authors.initials: C
authors.lastname: Peterson
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D004273:DNA, Neoplasm / Q000145:classification / Q000235:genetics; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics*; D009415:Nerve Net; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods; D017421:Sequence Analysis / Q000379:methods
chemical_list: D004273:DNA, Neoplasm