MCPNet: a parallel maximum capacity-based genome-scale gene network construction framework.

ABSTRACT:

Motivation

Gene network reconstruction from gene expression profiles is a compute- and data-intensive problem. Numerous methods based on diverse approaches including mutual information, random forests, Bayesian networks, correlation measures, as well as their transforms and filters such as data processing inequality, have been proposed. However, an effective gene network reconstruction method that performs well in all three aspects of computational efficiency, data size scalability, and output quality remains elusive. Simple techniques such as Pearson correlation are fast to compute but ignore indirect interactions, while more robust methods such as Bayesian networks are prohibitively time consuming to apply to tens of thousands of genes.

Results

We developed maximum capacit

SUBMITTER: Pan TC 

PROVIDER: S-EPMC10287961 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature