title: A two-layer integration framework for protein complex detection.
abstract: BACKGROUND
  Protein complexes carry out nearly all signaling and functional processes within cells. The study of protein complexes is an effective strategy to analyze cellular functions and biological processes. With the increasing availability of proteomics data, various computational methods have recently been developed to predict protein complexes. However, different computational methods are based on their own assumptions and designed to work on different data sources, and various biological screening methods have their unique experiment conditions, and are often different in scale and noise level. Therefore, a single computational method on a specific data source is generally not able to generate comprehensive and reliable prediction results.
  
  
  RESULTS
  In this paper, we develop a novel Two-layer INtegrative Complex Detection (TINCD) model to detect protein complexes, leveraging the information from both clustering results and raw data sources. In particular, we first integrate various clustering results to construct consensus matrices for proteins to measure their overall co-complex propensity. Second, we combine these consensus matrices with the co-complex score matrix derived from Tandem Affinity Purification/Mass Spectrometry (TAP) data and obtain an integrated co-complex similarity network via an unsupervised metric fusion method. Finally, a novel graph regularized doubly stochastic matrix decomposition model is proposed to detect overlapping protein complexes from the integrated similarity network.
  
  
  CONCLUSIONS
  Extensive experimental results demonstrate that TINCD performs much better than 21 state-of-the-art complex detection techniques, including ensemble clustering and data integration techniques.
authors.affiliation: College of Information Engineering, Shenzhen University, Shenzhen, 518060, China. ouyangle@mail2.sysu.edu.cn.|Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China. ouyangle@mail2.sysu.edu.cn.|Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. ouyangle@mail2.sysu.edu.cn.
authors.forename: Le
authors.initials: L
authors.lastname: Ou-Yang
authors.affiliation: Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore, Singapore. wumin@i2r.a-star.edu.sg.
authors.forename: Min
authors.initials: M
authors.lastname: Wu
authors.affiliation: School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Wuhan, 430079, China. zhangxf9@mail2.sysu.edu.cn.
authors.forename: Xiao-Fei
authors.initials: XF
authors.lastname: Zhang
authors.affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.
authors.forename: Dao-Qing
authors.initials: DQ
authors.lastname: Dai
authors.affiliation: Institute for Infocomm Research (I2R), A*STAR, 1 Fusionopolis Way, Singapore, Singapore. xlli@i2r.a-star.edu.sg.
authors.forename: Xiao-Li
authors.initials: XL
authors.lastname: Li
authors.affiliation: Department of Electronic Engineering, City University of Hong Kong, Hong Kong, China. h.yan@cityu.edu.hk.
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins