title: Classification algorithms for phenotype prediction in genomics and proteomics.
abstract: This paper gives an overview of statistical and machine learning-based feature selection and pattern classification algorithms and their application in molecular cancer classification or phenotype prediction. In particular, the paper focuses on the use of these computational methods for gene and peak selection from microarray and mass spectrometry data, respectively. The selected features are presented to a classifier for phenotype prediction.
authors.affiliation: Lombardi Comprehensive Cancer Center, 3970 Reservoir Rd NW, Washington, DC 20057, USA. hwr@georgetown.edu
authors.forename: Habtom W
authors.initials: HW
authors.lastname: Ressom
authors.forename: Rency S
authors.initials: RS
authors.lastname: Varghese
authors.forename: Zhen
authors.initials: Z
authors.lastname: Zhang
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
authors.forename: Robert
authors.initials: R
authors.lastname: Clarke
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D023281:Genomics / Q000379:methods*; D006801:Humans; D013058:Mass Spectrometry / Q000379:methods; D015233:Models, Statistical; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated; D010641:Phenotype; D040901:Proteomics / Q000379:methods*