A mathematical programming approach for gene selection and tissue classification.
BACKGROUND
Extracting useful information from expression levels of thousands of genes generated with microarray technology needs a variety of analytical techniques. Mathematical programming approaches for classification analysis outperform parametric methods when the data depart from assumptions underlying these methods. Therefore, a mathematical programming approach is developed for gene selection and tissue classification using gene expression profiles.


RESULTS
A new mixed integer programming model is formulated for this purpose. The mixed integer programming model simultaneously selects genes and constructs a classification model to classify two groups of tissue samples as accurately as possible. Very encouraging results were obtained with two data sets from the literature as examples. These results show that the mathematical programming approach can rival or outperform traditional classification methods.
Sun
Minghe
M
Department of Management Science and Statistics, College of Business, The University of Texas at San Antonio, San Antonio, TX 78249-0632, USA. msun@utsa.edu
Xiong
Momiao
M
D000465:Algorithms; D016000:Cluster Analysis; D003239:Connective Tissue Cells / Q000145:classification*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics; D008957:Models, Genetic; D009363:Neoplasm Proteins / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification / Q000235:genetics; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017421:Sequence Analysis / Q000379:methods*
D009363:Neoplasm Proteins