Cited by 418Bioinformatics and Genomic NetworksBiomedical Text Mining and OntologiesGenomics and Rare Diseases

Computational tools for prioritizing candidate genes: boosting disease gene discovery

Moreau, Yves, Tranchevent, Léon-Charles·2012·Nature Reviews Genetics

Abstract

At different stages of any research project, molecular biologists need to choose - often somewhat arbitrarily, even after careful statistical data analysis - which genes or proteins to investigate further experimentally and which to leave out because of limited resources. Computational methods that integrate complex, heterogeneous data sets - such as expression data, sequence information, functional annotation and the biomedical literature - allow prioritizing genes for future study in a more informed way. Such methods can substantially increase the yield of downstream studies and are becoming invaluable to researchers.

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Crossref · 10.1038/nrg3253

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133 of 133 references verified0 citation errors · 0 not checkable

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  • Topology-based methods have been shown to capture and use biological system dynamics to enrich existing PPI networks.

    “Topology-based methods have been shown to capture and leverage the dynamics of biological systems to enrich existing PPI networks [119].”

    From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [119]

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Authors
Moreau, Y., & Tranchevent, L.-C.
Affiliations
KU Leuven (BE)
Corresponding author
  • Moreau, Yves
  • Tranchevent, Léon-Charles
Venue
Nature Reviews Genetics
Published
2012-07-03
Volume
13
Issue
8
Pages
523-536
ISSN
  • 1471-0056 (ISSN-L)
  • 1471-0064
OpenAlex
W2090247362
Cited by
417
References
134
Citations by year
2026: 5 · 2025: 4 · 2024: 6 · 2023: 15 · 2022: 19 · 2021: 21 · 2020: 31 · 2019: 36 · 2018: 36 · 2017: 42 · 2016: 45 · 2015: 49 · 2014: 52 · 2013: 41 · 2012: 15
FWCI
4.58
Type
journal article
Language
en
Open access
closed
Locations
  • Nature Reviews Genetics (publishedVersion)
  • PubMed (publishedVersion)
Funders
FP7 Health
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
Keywords
gene prioritization, candidate genes, disease gene discovery, computational tools, heterogeneous data integration, expression data, functional annotation, biomedical text mining, genetic linkage, omics data
MeSH terms
Animals, Humans, Models, Genetic, Computational Biology, Genetic Predisposition to Disease, Databases, Genetic, Mice, Genetic Association Studies, Haploinsufficiency

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