Non-parametric statistical tests for informative gene selection

Jinwen Ma, Fuhai Li, Jianfeng Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents two non-parametric statistical test methods, called Kolmogorov-Smirnov (KS) and U statistic test methods, respectively, for informative gene selection of a tumor from microarray data, with help of the theory of false discovery rate. To test the effectiveness of these non-parametric statistical test methods, we use the support vector machine (SVM) to construct a tumor diagnosis system (i.e., a binary classifier) based on the identified informative genes on the colon and leukemia data. It is shown by the experiments that the constructed tumor diagnosis system with both the KS and U statistic test methods can reach a good prediction accuracy on both the colon and leukemia data sets.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science
EditorsJ. Wang, X. Liao, Z. Yi
Pages697-702
Number of pages6
Volume3498
EditionIII
StatePublished - 2005
EventSecond International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005 - Chongqing, China
Duration: May 30 2005Jun 1 2005

Other

OtherSecond International Symposium on Neural Networks: Advances in Neural Networks - ISNN 2005
CountryChina
CityChongqing
Period5/30/056/1/05

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

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