Recursive Partitioning for Tumor Classification with Gene Expression Microarray Data

Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 98; no. 12; pp. 6730 - 6735
Main Authors Zhang, Heping, Yu, Chang-Yung, Singer, Burton, Xiong, Momiao
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 05.06.2001
National Acad Sciences
The National Academy of Sciences
Subjects
Biological Sciences
Cancer
Classification
Classification systems
Colorectal cancer
Correlations
Error rates
Gene expression
Gene Expression Profiling
Genetic vectors
Genetics
Humans
Information classification
Neoplasms - classification
Neoplasms - genetics
Oligonucleotide Array Sequence Analysis
Tissue samples
Tumors
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Summary:Precise classification of tumors is critically important for cancer diagnosis and treatment. It is also a scientifically challenging task. Recently, efforts have been made to use gene expression profiles to improve the precision of classification, with limited success. Using a published data set for purposes of comparison, we introduce a methodology based on classification trees and demonstrate that it is significantly more accurate for discriminating among distinct colon cancer tissues than other statistical approaches used heretofore. In addition, competing classification trees are displayed, which suggest that different genes may coregulate colon cancers.
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To whom reprint requests should be addressed. E-mail: heping.zhang@yale.edu.
Contributed by Burton Singer
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.111153698