Development of a Knowledge-Based Multi-Scheme Cancer Microarray Data Analysis System

Authors:
John H. Phan;Chang F. Quo;Kejiao Guo;Weimin Feng;Geoffrey Wang;May D. Wang
Affiliations:
Georgia Institute of Technology and Emory University;Georgia Institute of Technology and Emory University;Georgia Institute of Technology and Emory University;Georgia Institute of Technology and Emory University;Georgia Institute of Technology;Georgia Institute of Technology and Emory University
Venue:
CSB '04 Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference
Year:
2004

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A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery

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Abstract

Comparing genes expressed in normal and diseased states assists the understanding of cancer pathophysiology, detection, prognosis, and therapeutic target study. Many existing expression analysis papers show that microarray data are usually case dependent, have small sample (patients) sizes, and have large gene dimensions. Thus, we have been developing a robust multi-parameter, multi-scheme knowledge-based optimization system that integrates the strengths of statistics, pattern-recognition, and support vector machines (SVM). The optimization logic identifies optimal cancer signature genes by utilizing different analysis models based on unsupervised and supervised clustering. Our system is being finalized by testing over public and in-house datasets with the intention of validation through clinical knowledge feedback.