Gene Selection for Cancer Classification using Support Vector Machines

被引：324

作者：

Isabelle Guyon

Jason Weston

Stephen Barnhill

Vladimir Vapnik

机构：

[1] AT&T Labs,

来源：

Machine Learning | 2002年 / 46卷

关键词：

diagnosis; diagnostic tests; drug discovery; RNA expression; genomics; gene selection; DNA micro-array; proteomics; cancer classification; feature selection; support vector machines; recursive feature elimination;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

DNA micro-arrays now permit scientists to screen thousands of genes simultaneously and determine whether those genes are active, hyperactive or silent in normal or cancerous tissue. Because these new micro-array devices generate bewildering amounts of raw data, new analytical methods must be developed to sort out whether cancer tissues have distinctive signatures of gene expression over normal tissues or other types of cancer tissues.

引用

页码：389 / 422

页数：33

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