Assessing and combining reliability of protein interaction sources.
Integrating diverse sources of interaction information to create protein networks requires strategies sensitive to differences in accuracy and coverage of each source. Previous integration approaches calculate reliabilities of protein interaction information sources based on congruity to a designated 'gold standard.' In this paper, we provide a comparison of the two most popular existing approaches and propose a novel alternative for assessing reliabilities which does not require a gold standard. We identify a new method for combining the resultant reliabilities and compare it against an existing method. Further, we propose an extrinsic approach to evaluation of reliability estimates, considering their influence on the downstream tasks of inferring protein function and learning regulatory networks from expression data. Results using this evaluation method show 1) our method for reliability estimation is an attractive alternative to those requiring a gold standard and 2) the new method for combining reliabilities is less sensitive to noise in reliability assignments than the similar existing technique.
Leach
Sonia
S
University of Colorado at Denver, Health Sciences Center, Aurora, CO 80045, USA. Sonia.Leach@uchsc.edu
Gabow
Aaron
A
Hunter
Lawrence
L
Goldberg
Debra S
DS
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D019295:Computational Biology; D030541:Databases, Genetic; D005800:Genes, Fungal; D016013:Likelihood Functions; D051379:Mice; D025941:Protein Interaction Mapping / Q000706:statistics & numerical data*; D015203:Reproducibility of Results