Enhancing the accuracy of HMM-based conserved pathway prediction using global correspondence scores.

ABSTRACT:

Background

Comparative network analysis aims to identify common subnetworks in biological networks. It can facilitate the prediction of conserved functional modules across different species and provide deep insights into their underlying regulatory mechanisms. Recently, it has been shown that hidden Markov models (HMMs) can provide a flexible and computationally efficient framework for modeling and comparing biological networks.

Results

In this work, we show that using global correspondence scores between molecules can improve the accuracy of the HMM-based network alignment results. The global correspondence scores are computed by performing a semi-Markov random walk on the networks to be compared. The resulting score naturally integrates the sequence similarity between mole

SUBMITTER: Qian X 

PROVIDER: S-EPMC3236849 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature