title: Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics.
abstract: The hypergeometric enrichment analysis approach typically fares poorly in feature-selection stability due to its upstream reliance on the t-test to generate differential protein lists before testing for enrichment on a protein complex, subnetwork or gene group.
  
  
  
  Swapping the t-test in favour of a fuzzy rank-based weight system similar to that used in network-based methods like Quantitative Proteomics Signature Profiling (QPSP), Fuzzy SubNets (FSNET) and paired FSNET (PFSNET) produces dramatic improvements.
  
  
  
  This approach, Fuzzy-FishNET, exhibits high precision-recall over three sets of simulated data (with simulated protein complexes) while excelling in feature-selection reproducibility on real data (based on evaluation with real protein complexes). Overlap comparisons with PFSNET shows Fuzzy-FishNET selects the most significant complexes, which are also strongly class-discriminative. Cross-validation further demonstrates Fuzzy-FishNET selects class-relevant protein complexes.
  
  
  
  Based on evaluation with simulated and real datasets, Fuzzy-FishNET is a significant upgrade of the traditional hypergeometric enrichment approach and a powerful new entrant amongst comparative proteomics analysis methods.
authors.affiliation: School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, People's Republic of China. goh.informatics@gmail.com.
authors.forename: Wilson Wen Bin
authors.initials: WW
authors.lastname: Goh
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D007680:Kidney Neoplasms / Q000737:chemistry; D011506:Proteins / Q000737:chemistry*; D040901:Proteomics / Q000379:methods*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins
keywords: Bioinformatics; FSNET; GSEA; Networks; PFSNET; Proteomics; QPSP; Renal Cancer; SNET