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]
Citations
Cites
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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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A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
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Network medicine: a network-based approach to human disease
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Cited by
Sorted by citations
STRING v9.1: protein-protein interaction networks, with increased coverage and integration
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398
Integrative omics for health and disease
Karczewski, K. J., & Snyder, M. P. (2018) · Nature Reviews Genetics · doi:10.1038/nrg.2018.4 · Cited by 1,158
Biological interpretation of genome-wide association studies using predicted gene functions
Pers, T. H., Karjalainen, J. M., Chan, Y., Westra, H.-J., Wood, A. R., Yang, J., Lui, J. C., Vedantam, S., Gustafsson, S., Esko, T., et al. (2015) · Nature Communications · doi:10.1038/ncomms6890 · Cited by 787
“and converting the adjusted z-score to a P value; and (3) repeating steps 1 and 2 twenty times to estimate experiment-wide FDRs similar to the method described above.”
BackgroundMethodHuman symptoms–disease network
Zhou, X., Menche, J., Barabási, A.-Ĺ., & Sharma, A. (2014) · Nature Communications · doi:10.1038/ncomms5212 · Cited by 659
“Interestingly, the inclusion of disease phenotype similarities can substantially improve the performance of candidate gene prediction methods 21–24 .”
BackgroundNext-generation diagnostics and disease-gene discovery with the Exomiser
Smedley, D., Jacobsen, J. O. B., Jäger, M., Köhler, S., Holtgrewe, M., Schubach, M., Siragusa, E., Zemojtel, T., Buske, O. J., Washington, N. L., et al. (2015) · Nature Protocols · doi:10.1038/nprot.2015.124 · Cited by 445
“…data sets, including expression data, genetic sequences, functional annotations, protein-protein interaction networks and information from the medical literature in order to derive a ranked list of genes that provide investigators clues about those genes that warrant closer investigation 14 .”
Background
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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