title: Integrating diverse biological and computational sources for reliable protein-protein interactions.
abstract: BACKGROUND
  Protein-protein interactions (PPIs) play important roles in various cellular processes. However, the low quality of current PPI data detected from high-throughput screening techniques has diminished the potential usefulness of the data. We need to develop a method to address the high data noise and incompleteness of PPI data, namely, to filter out inaccurate protein interactions (false positives) and predict putative protein interactions (false negatives).
  
  
  RESULTS
  In this paper, we proposed a novel two-step method to integrate diverse biological and computational sources of supporting evidence for reliable PPIs. The first step, interaction binning or InterBIN, groups PPIs together to more accurately estimate the likelihood (Bin-Confidence score) that the protein pairs interact for each biological or computational evidence source. The second step, interaction classification or InterCLASS, integrates the collected Bin-Confidence scores to build classifiers and identify reliable interactions.
  
  
  CONCLUSIONS
  We performed comprehensive experiments on two benchmark yeast PPI datasets. The experimental results showed that our proposed method can effectively eliminate false positives in detected PPIs and identify false negatives by predicting novel yet reliable PPIs. Our proposed method also performed significantly better than merely using each of individual evidence sources, illustrating the importance of integrating various biological and computational sources of data and evidence.
authors.affiliation: School of Computer Engineering, Nanyang Technological University, Singapore. wumi0002@ntu.edu.sg
authors.forename: Min
authors.initials: M
authors.lastname: Wu
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Li
authors.forename: Hon Nian
authors.initials: HN
authors.lastname: Chua
authors.forename: Chee-Keong
authors.initials: CK
authors.lastname: Kwoh
authors.forename: See-Kiong
authors.initials: SK
authors.lastname: Ng
mesh_terms: D019295:Computational Biology / Q000379:methods*; D025941:Protein Interaction Mapping / Q000379:methods; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics / Q000378:metabolism*; D012984:Software
chemical_list: D029701:Saccharomyces cerevisiae Proteins