A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns.
Feature selection plays an important role in classification. We present a comparative study on six feature selection heuristics by applying them to two sets of data. The first set of data are gene expression profiles from Acute Lymphoblastic Leukemia (ALL) patients. The second set of data are proteomic patterns from ovarian cancer patients. Based on features chosen by these methods, error rates of several classification algorithms were obtained for analysis. Our results demonstrate the importance of feature selection in accurately classifying new samples.
Laboratories for Information Technology, 21 Heng Mui Keng Terr, 119613 Singapore. huiqing@lit.a-star.edu.sg
Huiqing
H
Liu
Jinyan
J
Li
Limsoon
L
Wong
D000465:Algorithms; D003627:Data Interpretation, Statistical; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D010051:Ovarian Neoplasms / Q000145:classification / Q000235:genetics; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification / Q000235:genetics; D020543:Proteome / Q000145:classification / Q000235:genetics*
D020543:Proteome