ArticleJournal of computational biology : a journal of computational molecular cell biology2011
Vavien: an algorithm for prioritizing candidate disease genes based on topological similarity of proteins in interaction networks.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.
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Who cites it
40 citing papers in PubMed.
- MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction.Briefings in bioinformatics · 2026Article
- Knowledge-slanted random forest method for high-dimensional data and small sample size with a feature selection application for gene expression data.BioData mining · 2024Article
- Identification of gene biomarkers for brain diseases via multi-network topological semantics extraction and graph convolutional network.BMC genomics · 2024Article
- Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery.Briefings in bioinformatics · 2024Review
- HetIG-PreDiG: A Heterogeneous Integrated Graph Model for Predicting Human Disease Genes based on gene expression.PloS one · 2023Article
- Comprehensive Analysis of Electronic Health Records to Characterize The Association Between Intimate Partner Violence and Mental Health.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- An integrated network representation of multiple cancer-specific data for graph-based machine learning.NPJ systems biology and applications · 2022Article
- A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Network propagation-based prioritization of long tail genes in 17 cancer types.Genome biology · 2021Article
- Ranking cancer drivers via betweenness-based outlier detection and random walks.BMC bioinformatics · 2021Article
- Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus.Frontiers in genetics · 2021Article
- DriveWays: a method for identifying possibly overlapping driver pathways in cancer.Scientific reports · 2020Article
- Knowledge-guided analysis of "omics" data using the KnowEnG cloud platform.PLoS biology · 2020Article
- Integrated querying and version control of context-specific biological networks.Database : the journal of biological databases and curation · 2020Article
- Review
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
- Bioinformatic Analysis Identifies Three Potentially Key Differentially Expressed Genes in Peripheral Blood Mononuclear Cells of Patients with Takayasu's Arteritis.Cell journal · 2018Article
- Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network.BMC systems biology · 2017Article
- Network propagation: a universal amplifier of genetic associations.Nature reviews. Genetics · 2017Review
- Linearity of network proximity measures: implications for set-based queries and significance testing.Bioinformatics (Oxford, England) · 2017Article
Corrections and comments
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3 authors.
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Abstract
Genome-wide linkage and association studies have demonstrated promise in identifying genetic factors that influence health and disease. An important challenge is to narrow down the set of candidate genes that are implicated by these analyses. Protein-protein interaction (PPI) networks are useful in extracting the functional relationships between known disease and candidate genes, based on the principle that products of genes implicated in similar diseases are likely to exhibit significant connectivity/proximity. Information flow?based methods are shown to be very effective in prioritizing candidate disease genes. In this article, we utilize the topology of PPI networks to infer functional information in the context of disease association. Our approach is based on the assumption that PPI networks are organized into recurrent schemes that underlie the mechanisms of cooperation among different proteins. We hypothesize that proteins associated with similar diseases would exhibit similar topological characteristics in PPI networks. Utilizing the location of a protein in the network with respect to other proteins (i.e., the "topological profile" of the proteins), we develop a novel measure to assess the topological similarity of proteins in a PPI network. We then use this measure to prioritize candidate disease genes based on the topological similarity of their products and the products of known disease genes. We test the resulting algorithm, Vavien, via systematic experimental studies using an integrated human PPI network and the Online Mendelian Inheritance in Man (OMIM) database. Vavien outperforms other network-based prioritization algorithms as shown in the results and is available at www.diseasegenes.org.
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