Exploiting inter-gene information for microarray data integration
Proceedings of the 2007 ACM symposium on Applied computing
Cross-generation and cross-laboratory predictions of Affymetrix microarrays by rank-based methods
Journal of Biomedical Informatics
Novel Extension of k - TSP Algorithm for Microarray Classification
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
A cube framework for incorporating inter-gene information into biological data mining
International Journal of Data Mining and Bioinformatics
Cross-platform microarray data integration using the Normalised Linear Transform
International Journal of Data Mining and Bioinformatics
Extraction of informative genes from integrated microarray data
ISMIS'08 Proceedings of the 17th international conference on Foundations of intelligent systems
Independent component analysis: Mining microarray data for fundamental human gene expression modules
Journal of Biomedical Informatics
Expert Systems with Applications: An International Journal
Identification of biomarkers for prostate cancer prognosis using a novel two-step cluster analysis
PRIB'11 Proceedings of the 6th IAPR international conference on Pattern recognition in bioinformatics
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Motivation: DNA microarray data analysis has been used previously to identify marker genes which discriminate cancer from normal samples. However, due to the limited sample size of each study, there are few common markers among different studies of the same cancer. With the rapid accumulation of microarray data, it is of great interest to integrate inter-study microarray data to increase sample size, which could lead to the discovery of more reliable markers. Results: We present a novel, simple method of integrating different microarray datasets to identify marker genes and apply the method to prostate cancer datasets. In this study, by applying a new statistical method, referred to as the top-scoring pair (TSP) classifier, we have identified a pair of robust marker genes (HPN and STAT6) by integrating microarray datasets from three different prostate cancer studies. Cross-platform validation shows that the TSP classifier built from the marker gene pair, which simply compares relative expression values, achieves high accuracy, sensitivity and specificity on independent datasets generated using various array platforms. Our findings suggest a new model for the discovery of marker genes from accumulated microarray data and demonstrate how the great wealth of microarray data can be exploited to increase the power of statistical analysis. Contact: leixu@jhu.edu