Bayesian inference for genomic data integration reduces misclassification rate in predicting protein-protein interactions.

ABSTRACT: Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased s

SUBMITTER: Xing C 

PROVIDER: S-EPMC3145649 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature