title: An extensive analysis of disease-gene associations using network integration and fast kernel-based gene prioritization methods.
abstract: OBJECTIVE
  In the context of "network medicine", gene prioritization methods represent one of the main tools to discover candidate disease genes by exploiting the large amount of data covering different types of functional relationships between genes. Several works proposed to integrate multiple sources of data to improve disease gene prioritization, but to our knowledge no systematic studies focused on the quantitative evaluation of the impact of network integration on gene prioritization. In this paper, we aim at providing an extensive analysis of gene-disease associations not limited to genetic disorders, and a systematic comparison of different network integration methods for gene prioritization.
  
  
  METHODS
  We collected nine different functional networks representing different functional relationships between genes, and we combined them through both unweighted and weighted network integration methods. We then prioritized genes with respect to each of the considered 708 medical subject headings (MeSH) diseases by applying classical guilt-by-association, random walk and random walk with restart algorithms, and the recently proposed kernelized score functions.
  
  
  RESULTS
  The results obtained with classical random walk algorithms and the best single network achieved an average area under the curve (AUC) across the 708 MeSH diseases of about 0.82, while kernelized score functions and network integration boosted the average AUC to about 0.89. Weighted integration, by exploiting the different "informativeness" embedded in different functional networks, outperforms unweighted integration at 0.01 significance level, according to the Wilcoxon signed rank sum test. For each MeSH disease we provide the top-ranked unannotated candidate genes, available for further bio-medical investigation.
  
  
  CONCLUSIONS
  Network integration is necessary to boost the performances of gene prioritization methods. Moreover the methods based on kernelized score functions can further enhance disease gene ranking results, by adopting both local and global learning strategies, able to exploit the overall topology of the network.
authors.affiliation: AnacletoLab - Dipartimento di Informatica, Università degli Studi di Milano, via Comelico 39/41, 20135 Milano, Italy. Electronic address: valentini@di.unimi.it.
authors.forename: Giorgio
authors.initials: G
authors.lastname: Valentini
authors.affiliation: Department of Computer Science and Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham TW20 0EX, UK.
authors.forename: Alberto
authors.initials: A
authors.lastname: Paccanaro
authors.affiliation: Department of Computer Science and Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham TW20 0EX, UK.
authors.forename: Horacio
authors.initials: H
authors.lastname: Caniza
authors.affiliation: Department of Computer Science and Centre for Systems and Synthetic Biology, Royal Holloway, University of London, Egham TW20 0EX, UK.
authors.forename: Alfonso E
authors.initials: AE
authors.lastname: Romero
authors.affiliation: AnacletoLab - Dipartimento di Informatica, Università degli Studi di Milano, via Comelico 39/41, 20135 Milano, Italy.
authors.forename: Matteo
authors.initials: M
authors.lastname: Re
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods*; D006801:Humans; D046650:Medical Subject Headings
keywords: Gene disease prioritization; Heterogeneous data fusion; MeSH descriptors; Network integration; Node label ranking
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