Inferring high-confidence human protein-protein interactions.

ABSTRACT:

Background

As numerous experimental factors drive the acquisition, identification, and interpretation of protein-protein interactions (PPIs), aggregated assemblies of human PPI data invariably contain experiment-dependent noise. Ascertaining the reliability of PPIs collected from these diverse studies and scoring them to infer high-confidence networks is a non-trivial task. Moreover, a large number of PPIs share the same number of reported occurrences, making it impossible to distinguish the reliability of these PPIs and rank-order them. For example, for the data analyzed here, we found that the majority (>83%) of currently available human PPIs have been reported only once.

Results

In this work, we proposed an unsupervised statistical approach to score a set of diverse, expe

SUBMITTER: Yu X 

PROVIDER: S-EPMC3416704 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature