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

@article{citeulike:1123221, abstract = {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}, address = {Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560012, India.}, author = {Shevade, S. K. and Keerthi, S. S.}, citeulike-article-id = {1123221}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/14630653}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=14630653}, day = {22}, issn = {1367-4803}, journal = {Bioinformatics}, keywords = {association, marker, microarray, snp, statistic}, month = {November}, number = {17}, pages = {2246--2253}, posted-at = {2007-02-26 16:57:29}, priority = {2}, title = {A simple and efficient algorithm for gene selection using sparse logistic regression.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/14630653}, volume = {19}, year = {2003} }

TY - JOUR ID - citeulike:1123221 L3 - citeulike-article-id:1123221 N2 - 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 IS - 17 JF - Bioinformatics SN - 1367-4803 AD - Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560012, India. EP - 2253 TI - A simple and efficient algorithm for gene selection using sparse logistic regression. VL - 19 SP - 2246 KW - association KW - marker KW - microarray KW - snp KW - statistic AU - Shevade, SK AU - Keerthi, SS PY - 2003/11/22/ UR - http://view.ncbi.nlm.nih.gov/pubmed/14630653 ER -