Heterogeneous gene data for classifying tumors

Y.M. Fung, Vincent To Yee Ng

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic research

Abstract

When classifying tumors using gene expression data, mining tasks commonly make use of only a single data set. However, classification models based on patterns extracted from a single data set are often not indicative of an entire population and heterogeneous samples subsequently applied to these models may not fit, leading to performance degradation. In short, it is not possible to guarantee that mining results based on a single gene expression data set will be reliable or robust (Miller et al., 2002). This problem can be addressed using classification algorithms capable of handling multiple, heterogeneous gene expression data sets. Apart from improving mining performance, the use of such algorithms would make mining results less sensitive to the variations of different microarray platforms and to experimental conditions embedded in heterogeneous gene expression data sets.
Original languageEnglish
Title of host publicationEncyclopedia of data warehousing and mining
PublisherIdea Group Publishing
Pages550-554
Number of pages5
ISBN (Print)1591405572, 9781591405573
DOIs
Publication statusPublished - 2005

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