Using the GEMS system for cancer diagnosis and biomarker discovery from microarray gene expression data

Authors:
Alexander Statnikov;Ioannis Tsamardinos;Constantin F. Aliferis
Affiliations:
Discovery System Laboratory, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN;Discovery System Laboratory, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN;Discovery System Laboratory, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN
Venue:
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Year:
2005

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A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

Bioinformatics

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Abstract

We will demonstrate the GEMS system for automated development and evaluation of high-quality cancer diagnostic models and biomarker discovery from microarray gene expression data. The development of GEMS was informed by the results of an extensive algorithmic evaluation using 11 microarray datasets. The system was further evaluated in two cross-dataset applications and using 5 microarray datasets. The performance of models produced by GEMS is comparable or better than the results obtained by human analysts, and these models generalize well to independent samples in cross-dataset applications. The system is freely available for download from http://www.gems-system.org for noncommercial use.