Classification algorithms for phenotype prediction in genomics and proteomics.
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.
Ressom
Habtom W
HW
Lombardi Comprehensive Cancer Center, 3970 Reservoir Rd NW, Washington, DC 20057, USA. hwr@georgetown.edu
Varghese
Rency S
RS
Zhang
Zhen
Z
Xuan
Jianhua
J
Clarke
Robert
R
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*