A simple and efficient algorithm for gene selection using sparse logistic regression | Zendy

Shirish Shevade | Zendy; S. Sathiya Keerthi | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Shirish Shevade,

S. Sathiya Keerthi

Publication year - 2003

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btg308

Subject(s) - computer science , software , simple (philosophy) , algorithm , context (archaeology) , classifier (uml) , gene selection , selection (genetic algorithm) , data mining , machine learning , artificial intelligence , microarray analysis techniques , biology , gene , paleontology , philosophy , gene expression , biochemistry , epistemology , programming language

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research