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 proteo...

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Published inGenome Informatics Vol. 13; pp. 51 - 60
Main Authors Wong, Limsoon, Liu, Huiqing, Li, Jinyan
Format Journal Article
LanguageEnglish
Published Japan Japanese Society for Bioinformatics 2002
Subjects
Algorithms
classification
Data Interpretation, Statistical
feature selection
Female
gene expression profiles
Gene Expression Profiling - methods
Humans
Ovarian Neoplasms - classification
Ovarian Neoplasms - genetics
Precursor Cell Lymphoblastic Leukemia-Lymphoma - classification
Precursor Cell Lymphoblastic Leukemia-Lymphoma - genetics
Proteome - classification
Proteome - genetics
proteomic patterns
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Summary: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.
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ISSN:0919-9454
2185-842X
DOI:10.11234/gi1990.13.51