Data Mining in Genomics

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.