A simple and efficient algorithm for gene selection using sparse logistic regression

• Published in: Bioinformatics Vol. 19; no. 17; pp. 2246 - 2253
• Main Authors: Shevade, S. K., Keerthi, S. S.
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 22.11.2003; Oxford Publishing Limited (England)
• Subjects: Algorithms; Biological and medical sciences; Biomarkers, Tumor - genetics; Breast Neoplasms - classification; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Cluster Analysis; Colonic Neoplasms - classification; Colonic Neoplasms - diagnosis; Colonic Neoplasms - genetics; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Genetic Testing - methods; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Neoplasms - classification; Neoplasms - diagnosis; Neoplasms - genetics; Oligonucleotide Array Sequence Analysis - methods; Pattern Recognition, Automated; Regression Analysis; Reproducibility of Results; Sensitivity and Specificity; Logistic regression; Gene; Selection; Gauss method; Software; Marker; Malignant tumor; Algorithm; Mathematical programming
• ISSN: 1367-4803; 1460-2059; 1367-4811
• DOI: 10.1093/bioinformatics/btg308

Motivation: This paper gives a new and efficient algorithm for the sparse logistic regression problem. The proposed algorithm is based on the Gauss–Seidel method and is asymptotically convergent. It is simple and extremely easy to implement; it neither uses any sophisticated mathematical programming software nor needs any matrix operations. It can be applied to a variety of real-world problems like identifying marker genes and building a classifier in the context of cancer diagnosis using microarray data. Results: The gene selection method suggested in this paper is demonstrated on two real-world data sets and the results were found to be consistent with the literature. Availability: The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml Supplementary Information: Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml