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


BACKGROUND
The implementation of this algorithm is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml


BACKGROUND
Supplementary material is available at the site http://guppy.mpe.nus.edu.sg/~mpessk/SparseLOGREG.shtml
Shevade
S K
SK
Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560012, India.
Keerthi
S S
SS
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics; D001943:Breast Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D016000:Cluster Analysis; D003110:Colonic Neoplasms / Q000145:classification / Q000175:diagnosis / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D005820:Genetic Testing / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D014408:Biomarkers, Tumor