We describe a novel probabilistic approach to estimating errors in two-hybrid (2H)

experiments. Such experiments are frequently used to elucidate protein-protein

interaction networks in a high-throughput fashion; however, a significant challenge

with these is their relatively high error rate, specifically, a high false-positive rate.

We describe a comprehensive error model for 2H data, accounting for both random

and systematic errors. The latter arise from limitations of the 2H experimental

protocol: in theory, the reporting mechanism of a 2H experiment should be acti-

vated if and only if the two proteins being tested truly interact; in practice, even in

the absence of a true interaction, it may be activated by some proteins – either by

themselves or through promiscuous interaction with other proteins. We describe

a probabilistic relational model that explicitly models the above phenomenon and

use Markov Chain Monte Carlo (MCMC) algorithms to compute both the proba-

bility of an observed 2H interaction being true as well as the probability of indi-

vidual proteins being self-activating/promiscuous. This is the first approach that

explicitly models systematic errors in protein-protein interaction data; in contrast,

previous work on this topic has modeled errors as being independent and random.

By explicitly modeling the sources of noise in 2H systems, we find that we are

better able to make use of the available experimental data. In comparison with

Bader et al.’s method for estimating confidence in 2H predicted interactions, the

proposed method performed 5-10% better overall, and in particular regimes im-

proved prediction accuracy by as much as 76%.