title: The integration of weighted human gene association networks based on link prediction.
abstract: Physical and functional interplays between genes or proteins have important biological meaning for cellular functions. Some efforts have been made to construct weighted gene association meta-networks by integrating multiple biological resources, where the weight indicates the confidence of the interaction. However, it is found that these existing human gene association networks share only quite limited overlapped interactions, suggesting their incompleteness and noise.
  
  
  
  Here we proposed a workflow to construct a weighted human gene association network using information of six existing networks, including two weighted specific PPI networks and four gene association meta-networks. We applied link prediction algorithm to predict possible missing links of the networks, cross-validation approach to refine each network and finally integrated the refined networks to get the final integrated network.
  
  
  
  The common information among the refined networks increases notably, suggesting their higher reliability. Our final integrated network owns much more links than most of the original networks, meanwhile its links still keep high functional relevance. Being used as background network in a case study of disease gene prediction, the final integrated network presents good performance, implying its reliability and application significance. Our workflow could be insightful for integrating and refining existing gene association data.
authors.affiliation: Department of Mathematics, Logistical Engineering University, Chongqing, China.
authors.forename: Jian
authors.initials: J
authors.lastname: Yang
authors.affiliation: Department of Mathematics, Logistical Engineering University, Chongqing, China.
authors.forename: Tinghong
authors.initials: T
authors.lastname: Yang
authors.affiliation: Department of Mathematics, Logistical Engineering University, Chongqing, China.
authors.forename: Duzhi
authors.initials: D
authors.lastname: Wu
authors.affiliation: Department of Mathematics, Logistical Engineering University, Chongqing, China.
authors.forename: Limei
authors.initials: L
authors.lastname: Lin
authors.affiliation: Department of Mathematics, Logistical Engineering University, Chongqing, China.
authors.forename: Fan
authors.initials: F
authors.lastname: Yang
authors.affiliation: Department of Mathematics, Logistical Engineering University, Chongqing, China. zhaojanne@gmail.com.|Institute of Interdisciplinary Complex Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China. zhaojanne@gmail.com.
authors.forename: Jing
authors.identifier: 0000-0002-2439-2876
authors.initials: J
authors.lastname: Zhao
mesh_terms: D019295:Computational Biology / Q000379:methods*; D004194:Disease / Q000235:genetics; D053263:Gene Regulatory Networks; D006801:Humans; D025941:Protein Interaction Mapping
keywords: Gene association network; Link prediction; Network integration; Weighted network