ArticleBioinformatics (Oxford, England)2016
An integer programming framework for inferring disease complexes from network data.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Immune pathogenic response landscape of acute posterior multifocal placoid pigment epitheliopathy revealed by scRNA sequencing.Genes and immunity · 2025Article
- Functional stratification of cancer drugs through integrated network similarity.NPJ systems biology and applications · 2022Article
- NetCore: a network propagation approach using node coreness.Nucleic acids research · 2020Article
- Comparative Analysis of Normalization Methods for Network Propagation.Frontiers in genetics · 2019Article
- Constructing an integrated gene similarity network for the identification of disease genes.Journal of biomedical semantics · 2017Article
- Network propagation: a universal amplifier of genetic associations.Nature reviews. Genetics · 2017Review
- GLADIATOR: a global approach for elucidating disease modules.Genome medicine · 2017Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationUnraveling the molecular mechanisms that underlie disease calls for methods that go beyond the identification of single causal genes to inferring larger protein assemblies that take part in the disease process.
resultsHere, we develop an exact, integer-programming-based method for associating protein complexes with disease. Our approach scores proteins based on their proximity in a protein-protein interaction network to a prior set that is known to be relevant for the studied disease. These scores are combined with interaction information to infer densely interacting protein complexes that are potentially disease-associated. We show that our method outperforms previous ones and leads to predictions that are well supported by current experimental data and literature knowledge. AVAILABILITY AND IMPLEMENTATION: The datasets we used, the executables and the results are available at www.cs.tau.ac.il/roded/disease_complexes.zip CONTACT: roded@post.tau.ac.il.
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