The DOI matches its Crossref record; 133 of the 134 references it cites were found in a registry or an index.
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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Cytoscape: A software Environment for integrated models of biomolecular interaction networks
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Gene Ontology: tool for the unification of biology
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Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
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Items 401–418 of 418
Translating genetics into genomics: From QTL identification to candidate gene discovery in rice
Raza, Q. S., Sabar, M., Rashid, M. A. R., & Atif, R. M. (2022) · Elsevier eBooks · doi:10.1016/b978-0-323-85243-2.00016-7
Pathogenicity predicting algorithm of single amino acid variants
Bai, K., Liu, Z., Lv, X., Li, D., Chen, X., Li, X., & Zhou, J. (2022) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis58238.2022.00034
“Another mainstream approach is to evaluate the physical interactions between genes and numerous known disease-related genes including sequence homology, shared functional annotation, co-expression, and gene product interactions [7].”
BackgroundDynamic computing budget allocation for design ranking in stochastic complex systems
Hui, X. I. A. O., Zhihong, W. E. I., Gang, K. O. U., & Xianming, G. U. (2023) · Scientia Sinica Informationis · doi:10.1360/ssi-2022-0451
Linking genes to traits in Arabidopsis using systems genetics
Hartanto, M. (2023) · doi:10.18174/641121
Milano, M. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00065-8
Gene Prioritization Using Semantic Similarity
Pranckevičienė, E. (2019) · Elsevier eBooks · doi:10.1016/b978-0-323-95502-7.00346-8
Chang, H.-C., Chu, C.-P., Lin, S.-J., & Hsiao, C. K. (2020) · Figshare · doi:10.6084/m9.figshare.11978499
Ji, Y., Chen, R., Wang, Q., Wei, Q., Tao, R., & Li, B. (2022) · Figshare · doi:10.6084/m9.figshare.19642045
Chang, H.-C., Chu, C.-P., Lin, S.-J., & Hsiao, C. K. (2020) · Figshare · doi:10.6084/m9.figshare.11978502
Atrial fibrillation: genetic architecture and polygenic risk prediction
Yuan, S., Levin, M. G., & Larsson, S. C. (2026) · Heart · doi:10.1136/heartjnl-2025-326870
“26 Systematic functional validation is therefore needed.”
Étude de l'histoire évolutive des gènes dans les génomes de vertébrés
Péres, A. (2015) · theses.fr (ABES) · doi:10.70675/48d1b8e7z25eez400fzaef9zbe8da8ab3bcc
Système de recommandation basé sur les réseaux pour l'interprétation de résultats de métabolomique
Frainay, C. (2017) · theses.fr (ABES) · doi:10.70675/9839b5d1z7786z4d9dza0a1zb1cc016d9abc
Nath, S., Illa, S. K., Yata, V. K., Shukla, R., & Kolliputi, N. (2026) · Frontiers in Genetics · doi:10.3389/fgene.2026.1774902
“Genes prioritized by the exploratory ranking procedure were highlighted within the network to facilitate biological interpretation of the candidate region (Harrow et al., 2012; Moreau and Tranchevent, 2012; Lawrence et al., 2013; R Core Team, 2020).”
Zhao, Q., Zheng, H., Zhang, Y., Bi, J., & Sun, T. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.10.743862
“…genes, high-centrality nodes have long been read as the functionally most important [BarabÆsi and Albert, 1999, Albert, 2005, Jeong et al., 2001], a premise built into candidate-gene prioritisation and function-prediction pipelines alike [Moreau and Tranchevent, 2012, Mostafavi et al., 2008].”
Wang, Y., He, H., & Xin, Q. (2026) · Next research. · doi:10.1016/j.nexres.2026.102372
FAIRsharing record for: Biomedical Entity Expansion, Ranking, and Explorations
FAIRsharing Team, & Yue, Z. (2018) · FAIRsharing.org · doi:10.25504/fairsharing.f3dla1
HOODS: finding context-specific neighborhoods of proteins, chemicals and diseases
Pallejá, A., & Jensen, L. J. (2015) · PeerJ · doi:10.7717/peerj.1057
“We tested the performance of our algorithm on this task only to prove that it produces neighborhoods that are significantly better than random; it is not intended to compete with the many existing tools specifically designed for prioritizing disease genes (reviewed in Moreau & Tranchevent, 2012).”
MethodPranckevičienė, E. (2015) · uO Research (University of Ottawa) · doi:10.20381/ruor-2702
“If prioritization is being assessed with respect to ranking the whole genome, then the criterion of the top 5%-15% ranked genome genes containing a small subset of the test genes is also suggested as indicator of good performance [Moreau and Tranchevent, 2012].”
BackgroundMethodResultInfluential
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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