Methods for multi-category cancer diagnosis from gene expression data: a comprehensive evaluation to inform decision support system development.
Cancer diagnosis is a major clinical applications area of gene expression microarray technology. We are seeking to develop a system for cancer diagnostic model creation based on microarray data. In order to equip the system with the optimal combination of data modeling methods, we performed a comprehensive evaluation of several major classification algorithms, gene selection methods, and cross-validation designs using 11 datasets spanning 74 diagnostic categories (41 cancer types and 12 normal tissue types). The Multi-Category Support Vector Machine techniques by Crammer and Singer, Weston and Watkins, and one-versus-rest were found to be the best methods and they outperform other learning algorithms such as K-Nearest Neighbors and Neural Networks often to a remarkable degree. Gene selection techniques are shown to significantly improve classification performance. These results guided the development of a software system that fully automates cancer diagnostic model construction with quality on par with or better than previously published results derived by expert human analysts.
Statnikov
Alexander
A
Discovery Systems Laboratory, Department of Biomedical Informatics, Vanderbilt University, Nashville, TN 37232, USA. alexander.statnikov@vanderbilt.edu
Aliferis
Constantin F
CF
Tsamardinos
Ioannis
I
D000465:Algorithms; D003936:Diagnosis, Computer-Assisted; D005103:Expert Systems; D005163:Factor Analysis, Statistical; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000145:classification*; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated; D012984:Software