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Computational tools for prioritizing candidate genes: boosting disease gene discovery
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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“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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Faculty Opinions recommendation of Semantic prioritization of novel causative genomic variants.
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“Henceforth, we refer to such methods as proximity-plus-knowledge-based approaches [5].”
MethodDeepGenePrior: A deep learning model to prioritize genes affected by copy number variantsPreprint
Rahaie, Z., Rabiee, H. R., & Alinejad-Rokny, H. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.22.504862
Metadata
- 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
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Reviews Genetics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- FP7 Health
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Health Sciences › Medicine › Health Informatics › 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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