Optimal search-based gene subset selection for gene array cancer classification.
High dimensionality has been a major problem for gene array-based cancer classification. It is critical to identify marker genes for cancer diagnoses. We developed a framework of gene selection methods based on previous studies. This paper focuses on optimal search-based subset selection methods because they evaluate the group performance of genes and help to pinpoint global optimal set of marker genes. Notably, this paper is the first to introduce tabu search (TS) to gene selection from high-dimensional gene array data. Our comparative study of gene selection methods demonstrated the effectiveness of optimal search-based gene subset selection to identify cancer marker genes. TS was shown to be a promising tool for gene subset selection.
Artificial Intelligence Laboratory, Department of Management Information Systems, Eller College of Management, University of Arizona, Tucson, AZ 85721, USA. jiexun@eller.arizona.edu
Jiexun
J
Li
Hua
H
Su
Hsinchun
H
Chen
Bernard W
BW
Futscher
D001185:Artificial Intelligence; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000145:classification; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000032:analysis* / Q000145:classification; D009369:Neoplasms / Q000175:diagnosis* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D011786:Quality Control
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins