A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases
Open accessAbstract
Modern high-throughput experiments provide us with numerous potential associations between genes and diseases. Experimental validation of all the discovered associations, let alone all the possible interactions between them, is time-consuming and expensive. To facilitate the discovery of causative genes, various approaches for prioritization of genes according to their relevance for a given disease have been developed. In this article, we explain the gene prioritization problem and provide an overview of computational tools for gene prioritization. Among about a hundred of published gene prioritization tools, we select and briefly describe 14 most up-to-date and user-friendly. Also, we discuss the advantages and disadvantages of existing tools, challenges of their validation, and the directions for future research.
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Local methods tend to rank true candidate genes higher when provided a short list of candidate genes.
“This approach is grounded in the observation that, while methods that learn global network topology yield higher overall performance than local methods considering only local network information, the latter tend to rank true candidate genes higher when provided a short list of candidate genes110.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [110]
The Prioritization Strategies section (page unavailable) says local-network methods rank true top-ranked candidates higher, despite global-topology methods having better overall performance. It links the local methods’ advantage to diseases with few associated genes that are tightly connected in the network, not to being provided a short list of candidate genes. (LLM, conf=0.87)
Experimental — accuracy not yet measuredMethods that learn global network topology yield higher overall performance than local methods considering only local network information.
“This approach is grounded in the observation that, while methods that learn global network topology yield higher overall performance than local methods considering only local network information, the latter tend to rank true candidate genes higher when provided a short list of candidate genes110.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [110]
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References 195 · Cited by 46
Research works printed but not linked: 1
- [72]Zhu X, Ghahramani Z, Lafferty J. Semi-supervised learning using gaussian fields and harmonic functions. In: IN ICML, 2003:912–9. Zhu X Ghahramani Z Lafferty J IN ICML 2003 912 9 Semi-supervised learning using gaussian fields and harmonic functions
- Authors
- Zolotareva, O., & Kleine, M.
- Affiliations
- Bielefeld University (DE)
- Corresponding author
- Zolotareva, Olga
- ORCID
- Zolotareva, Olga
- Venue
- Journal of Integrative Bioinformatics
- Publisher
- Walter de Gruyter GmbH
- Published
- 2019-09-09
- Volume
- 16
- Issue
- 4
- ISSN
- 1432-4385 (ISSN-L)
- 1613-4516
- PMC
- PMC7074139
- OpenAlex
- W2972577058
- Cited by
- 46
- References
- 195
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 11 · 2023: 10 · 2022: 10 · 2021: 5 · 2020: 3 · 2018: 2
- FWCI
- 0.39
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Berichte aus der medizinischen Informatik und Bioinformatik/Journal of integrative bioinformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Publikationen an der Universität Bielefeld (Universität Bielefeld) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (GRK 1906)
- 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, monogenic disorders, complex diseases, computational tools, gene-disease association, tool validation
- MeSH terms
- Humans, Computational Biology, Genetic Diseases, Inborn, Gene Regulatory Networks, Genome-Wide Association Study
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