Bayesian integration of networks without gold standards.

ABSTRACT:

Motivation

Biological experiments give insight into networks of processes inside a cell, but are subject to error and uncertainty. However, due to the overlap between the large number of experiments reported in public databases it is possible to assess the chances of individual observations being correct. In order to do so, existing methods rely on high-quality 'gold standard' reference networks, but such reference networks are not always available.

Results

We present a novel algorithm for computing the probability of network interactions that operates without gold standard reference data. We show that our algorithm outperforms existing gold standard-based methods. Finally, we apply the new algorithm to a large collection of genetic interaction and protein-protein interactio

SUBMITTER: Weile J 

PROVIDER: S-EPMC3356839 | biostudies-literature | 2012 Jun

REPOSITORIES: biostudies-literature