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|Title:||A simple and efficient algorithm for gene selection using sparse logistic regression||Authors:||Shevade, S.K.
|Issue Date:||22-Nov-2003||Citation:||Shevade, S.K., Keerthi, S.S. (2003-11-22). A simple and efficient algorithm for gene selection using sparse logistic regression. Bioinformatics 19 (17) : 2246-2253. ScholarBank@NUS Repository. https://doi.org/10.1093/bioinformatics/btg308||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.||Source Title:||Bioinformatics||URI:||http://scholarbank.nus.edu.sg/handle/10635/59264||ISSN:||13674803||DOI:||10.1093/bioinformatics/btg308|
|Appears in Collections:||Staff Publications|
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