Work

Molecular classification of cancer types from microarray data using the combination of genetic algorithms and support vector machines

Year: 2003

Type: article

Abstract: Simultaneous multiclass classification of tumor types is essential for future clinical implementations of microarray-based cancer diagnosis. In this study, we have combined genetic algorithms (GAs) and all paired support vector machines (SVMs) for multiclass cancer identification. The predictive features have been selected through iterative SVMs/GAs, and recursive feature elimination post-processing steps, leading to a very compact cancer-related predictive gene set. Leave-one-out cross-validations yielded accuracies of 87.93% for the eight-class and 85.19% for the fourteen-class cancer classifications, outperforming the results derived from previously published methods.

Source: FEBS Letters

DOI: 10.1016/s0014-5793(03)01275-4

Authors: Sihua Peng, Qianghua Xu, Xuefeng Bruce Ling, Xiaoning Peng, Wei Du, Liangbiao Chen

Institutions: Zhejiang University, The University of Texas MD Anderson Cancer Center, Chinese Academy of Sciences, Institute of Genetics and Developmental Biology

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: National High-tech Research and Development Program, Institute of Genetics