A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns.
PubMedPublished 1 January 2002
Huiqing Liu, Jinyan Li, Limsoon Wong
Citations412
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TL;DR
This work presents a comparative study on six feature selection heuristics by applying them to two sets of data, which are gene expression profiles from Acute Lymphoblastic Leukemia and proteomic patterns from ovarian cancer patients.
Abstract
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.
Keywords
Computer ScienceBiochemistry, Genetics and Molecular Biology
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