Combining multiple positive training sets to generate confidence scores for protein-protein interactions.

ABSTRACT:

Motivation

High-throughput experimental and computational methods are generating a wealth of protein-protein interaction data for a variety of organisms. However, data produced by current state-of-the-art methods include many false positives, which can hinder the analyses needed to derive biological insights. One way to address this problem is to assign confidence scores that reflect the reliability and biological significance of each interaction. Most previously described scoring methods use a set of likely true positives to train a model to score all interactions in a dataset. A single positive training set, however, may be biased and not representative of true interaction space.

Results

We demonstrate a method to score protein interactions by utilizing multiple independen

SUBMITTER: Yu J 

PROVIDER: S-EPMC2638943 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature