GraphAlignment: Bayesian pairwise alignment of biological networks.

ABSTRACT:

Background

With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks.

Results

We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0.On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data

SUBMITTER: Kolar M 

PROVIDER: S-EPMC3573967 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature