Artif Intell Med 2017 Oct 4;82:34-46. Epub 2017 Sep 4.
Università Degli Studi di Milano, Dipartimento di Informatica, Via Comelico 39/41, Milano, Italy. Electronic address:
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Artif Intell Med 2014 Jun 20;61(2):63-78. Epub 2014 Mar 20.
AnacletoLab - Dipartimento di Informatica, Università degli Studi di Milano, via Comelico 39/41, 20135 Milano, Italy.
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. Read More
BMC Bioinformatics 2016 Nov 10;17(1):453. Epub 2016 Nov 10.
College of Information Sciences and Technology, Pennsylvania State University, 332 Information Sciences and Technology Building, University Park, 16802, PA, USA.
Background: Accurately prioritizing candidate disease genes is an important and challenging problem. Various network-based methods have been developed to predict potential disease genes by utilizing the disease similarity network and molecular networks such as protein interaction or gene co-expression networks. Although successful, a common limitation of the existing methods is that they assume all diseases share the same molecular network and a single generic molecular network is used to predict candidate genes for all diseases. Read More
PLoS One 2012 19;7(11):e49634. Epub 2012 Nov 19.
Knowledge Discovery and Bioinformatics Group, INESC-ID, Lisbon, Portugal.
Disease gene prioritization aims to suggest potential implications of genes in disease susceptibility. Often accomplished in a guilt-by-association scheme, promising candidates are sorted according to their relatedness to known disease genes. Network-based methods have been successfully exploiting this concept by capturing the interaction of genes or proteins into a score. Read More
BMC Bioinformatics 2014 Sep 24;15:315. Epub 2014 Sep 24.
Department of Molecular Biology and Genetics, Aarhus University, DK-8830 Tjele, Denmark.
Background: Prioritizing genetic variants is a challenge because disease susceptibility loci are often located in genes of unknown function or the relationship with the corresponding phenotype is unclear. A global data-mining exercise on the biomedical literature can establish the phenotypic profile of genes with respect to their connection to disease phenotypes. The importance of protein-protein interaction networks in the genetic heterogeneity of common diseases or complex traits is becoming increasingly recognized. Read More