This article reviews important emerging statistical concepts, data mining techniques,
and applications that have been recently developed and used for genomic data analysis.
First, general background and some critical issues in genomic data mining are summarized.
A novel concept of statistical significance is described, the so-called “false discovery
rate”—the rate of false-positives among all positive findings—which has been suggested
to control the error rate of numerous false-positives in large screening biological
data analysis. Two recent statistical testing methods are then introduced: significance
analysis of microarray and local pooled error tests. Statistical modeling in genomic
data analysis is then presented, such as analysis of variance and heterogeneous error
modeling approaches that have been suggested for analyzing microarray data obtained
from multiple experimental or biological conditions. Two sections then describe data
exploration and discovery tools largely termed as supervised learning and unsupervised
learning. The former approaches include several multivariate statistical methods to
investigate coexpression patterns of multiple genes, and the latter are the classification
methods to discover genomic biomarker signatures for predicting important subclasses
of human diseases. The last section briefly summarizes various genomic data mining
approaches in biomedical pathway analysis and patient outcome or chemotherapeutic
response prediction.
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Article info
Footnotes
This study is supported by NIH grant 1R01HL081690 of JKL.
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© 2008 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
