Filtering high-throughput protein-protein interaction data using a combination of genomic features.

ABSTRACT:

Background

Protein-protein interaction data used in the creation or prediction of molecular networks is usually obtained from large scale or high-throughput experiments. This experimental data is liable to contain a large number of spurious interactions. Hence, there is a need to validate the interactions and filter out the incorrect data before using them in prediction studies.

Results

In this study, we use a combination of 3 genomic features -- structurally known interacting Pfam domains, Gene Ontology annotations and sequence homology -- as a means to assign reliability to the protein-protein interactions in Saccharomyces cerevisiae determined by high-throughput experiments. Using Bayesian network approaches, we show that protein-protein interactions from high-throughput

SUBMITTER: Patil A 

PROVIDER: S-EPMC1127019 | biostudies-literature | 2005 Apr

REPOSITORIES: biostudies-literature