title: Predicting disease-related genes using integrated biomedical networks.
abstract: Identifying the genes associated to human diseases is crucial for disease diagnosis and drug design. Computational approaches, esp. the network-based approaches, have been recently developed to identify disease-related genes effectively from the existing biomedical networks. Meanwhile, the advance in biotechnology enables researchers to produce multi-omics data, enriching our understanding on human diseases, and revealing the complex relationships between genes and diseases. However, none of the existing computational approaches is able to integrate the huge amount of omics data into a weighted integrated network and utilize it to enhance disease related gene discovery.
  
  
  
  We propose a new network-based disease gene prediction method called SLN-SRW (Simplified Laplacian Normalization-Supervised Random Walk) to generate and model the edge weights of a new biomedical network that integrates biomedical data from heterogeneous sources, thus far enhancing the disease related gene discovery.
  
  
  
  The experiment results show that SLN-SRW significantly improves the performance of disease gene prediction on both the real and the synthetic data sets.
authors.affiliation: School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Jiajie
authors.initials: J
authors.lastname: Peng
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.|Current address: Tencent, Inc., Shenzhen, China.
authors.forename: Kun
authors.initials: K
authors.lastname: Bai
authors.affiliation: School of Computer Science, Northwestern Polytechnical University, Xi'an, China.
authors.forename: Xuequn
authors.initials: X
authors.lastname: Shang
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Guohua
authors.initials: G
authors.lastname: Wang
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China.
authors.forename: Hansheng
authors.initials: H
authors.lastname: Xue
authors.affiliation: Department of Mathematics, Harbin Institute of Technology, Harbin, China.
authors.forename: Shuilin
authors.initials: S
authors.lastname: Jin
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Liang
authors.initials: L
authors.lastname: Cheng
authors.affiliation: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China. ydwang@hit.edu.cn.
authors.forename: Yadong
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Institue of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, 40536, KY, USA. jinchen@msu.edu.|Department of Energy Plant Research Lab, Michigan State University, East Lansing, 48824, MI, USA. jinchen@msu.edu.
authors.forename: Jin
authors.initials: J
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D063990:Gene Ontology; D053263:Gene Regulatory Networks; D056726:Genetic Association Studies; D020022:Genetic Predisposition to Disease; D006801:Humans; D012372:ROC Curve; D015203:Reproducibility of Results; D057188:Workflow
keywords: Disease gene prediction; Integrated network; Laplacian normalization; Supervised random walk