Please use this identifier to cite or link to this item: https://doi.org/10.1093/bioinformatics/btg308
Title: A simple and efficient algorithm for gene selection using sparse logistic regression
Authors: Shevade, S.K.
Keerthi, S.S. 
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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