Mining gene expression data for positive and negative co-regulated gene clusters | Zendy

Liping Ji | Zendy; KianLee Tan | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Mining gene expression data for positive and negative co-regulated gene clusters

Author(s) -

Liping Ji,

KianLee Tan

Publication year - 2004

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/bth312

Subject(s) - apriori algorithm , association rule learning , data mining , a priori and a posteriori , computer science , gene , uncorrelated , software , association (psychology) , computational biology , biology , genetics , mathematics , statistics , philosophy , epistemology , programming language

Analysis of gene expression data can provide insights into the positive and negative co-regulation of genes. However, existing methods such as association rule mining are computationally expensive and the quality and quantities of the rules are sensitive to the support and confidence values. In this paper, we introduce the concept of positive and negative co-regulated gene cluster (PNCGC) that more accurately reflects the co-regulation of genes, and propose an efficient algorithm to extract PNCGCs.

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