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Updated: Aug 30, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
Published on: October 11, 2018
A simple and efficient algorithm for gene selection using sparse logistic regression
1Department of Computer Science and Automation, Indian Institute of Science, Bangalore 560012, India.
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
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