title: Comparison of statistical methods for subnetwork detection in the integration of gene expression and protein interaction network.
abstract: BACKGROUND
  With the advancement of high-throughput technologies and enrichment of popular public databases, more and more research focuses of bioinformatics research have been on computational integration of network and gene expression profiles for extracting context-dependent active subnetworks. Many methods for subnetwork searching have been developed. Scoring and searching algorithms present a range of computational considerations and implementations. The primary goal of present study is to comprehensively evaluate the performance of different subnetwork detection methods. Eleven popular methods were selected for comprehensive comparison.
  
  
  RESULTS
  First, taking into account the dependence of genes given a protein-protein interaction (PPI) network, we simulated microarray gene expression data under case and control conditions. Then each method was applied to the simulated data for subnetwork identification. Second, a large microarray data set of prostate cancer was used to assess the practical performance of each method. Using both simulation studies and a real data application, we evaluated the performance of different methods in terms of recall and precision.
  
  
  CONCLUSIONS
  jActiveModules, PinnacleZ and WMAXC performed well in identifying subnetwork with relative high precision and recall. BioNet performed very well only in precision. As none of methods outperformed other methods overall, users should choose an appropriate method based on the purposes of their studies.
authors.affiliation: Department of Biostatistics and Bioinformatics, Center for Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: He
authors.affiliation: Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
authors.forename: Dongdong
authors.initials: D
authors.lastname: Lin
authors.affiliation: Department of Biostatistics and Bioinformatics, Center for Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.
authors.forename: Jigang
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Department of Biostatistics and Bioinformatics, Center for Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA.|Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
authors.forename: Yu-Ping
authors.initials: YP
authors.lastname: Wang
authors.affiliation: Department of Biostatistics and Bioinformatics, Center for Bioinformatics and Genomics, Tulane University School of Public Health and Tropical Medicine, 1440 Canal St., Suite 2001, New Orleans, LA, 70112, USA. hdeng2@tulane.edu.
authors.forename: Hong-Wen
authors.initials: HW
authors.lastname: Deng
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008297:Male; D011471:Prostatic Neoplasms / Q000235:genetics / Q000378:metabolism*; D060066:Protein Interaction Maps; D059467:Transcriptome
keywords: Active subnetworks; Protein–protein interaction; Searching algorithms