title: Increasing confidence of protein interactomes using network topological metrics.
abstract: BACKGROUND
  Experimental limitations in high-throughput protein-protein interaction detection methods have resulted in low quality interaction datasets that contained sizable fractions of false positives and false negatives. Small-scale, focused experiments are then needed to complement the high-throughput methods to extract true protein interactions. However, the naturally vast interactomes would require much more scalable approaches.
  
  
  RESULTS
  We describe a novel method called IRAP* as a computational complement for repurification of the highly erroneous experimentally derived protein interactomes. Our method involves an iterative process of removing interactions that are confidently identified as false positives and adding interactions detected as false negatives into the interactomes. Identification of both false positives and false negatives are performed in IRAP* using interaction confidence measures based on network topological metrics. Potential false positives are identified amongst the detected interactions as those with very low computed confidence values, while potential false negatives are discovered as the undetected interactions with high computed confidence values. Our results from applying IRAP* on large-scale interaction datasets generated by the popular yeast-two-hybrid assays for yeast, fruit fly and worm showed that the computationally repurified interaction datasets contained potentially lower fractions of false positive and false negative errors based on functional homogeneity.
  
  
  BACKGROUND
  The confidence indices for PPIs in yeast, fruit fly and worm as computed by our method can be found at our website http://www.comp.nus.edu.sg/~chenjin/fpfn.
authors.affiliation: School of Computing, National University of Singapore, Singapore 119260.
authors.forename: Jin
authors.initials: J
authors.lastname: Chen
authors.forename: Wynne
authors.initials: W
authors.lastname: Hsu
authors.forename: Mong Li
authors.initials: ML
authors.lastname: Lee
authors.forename: See-Kiong
authors.initials: SK
authors.lastname: Ng
mesh_terms: D000818:Animals; D017173:Caenorhabditis elegans; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D030562:Databases, Protein; D004330:Drosophila; D005189:False Positive Reactions; D011485:Protein Binding; D025941:Protein Interaction Mapping; D040901:Proteomics / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000378:metabolism; D020539:Sequence Analysis, Protein; D012984:Software; D020798:Two-Hybrid System Techniques