AMELIE speeds Mendelian diagnosis by matching patient phenotype and genotype to primary literature
Open accessPublished version of a preprint
Abstract
An automated pipeline parses primary literature about Mendelian diseases to rank patient candidate causative genes, thereby accelerating diagnosis.
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There are limited approaches for phenotype-based diagnosis, and none automatically learn phenotypic and/or genotypic features over patient phenotype terms.
“While deep learning approaches exist for image-based diagnosis10,11, there are limited approaches for phenotype-based diagnosis, and none automatically learn phenotypic and/or genotypic features over patient phenotype terms23–28,44.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [26]
The Introduction (page unavailable) describes existing phenotype-based gene-ranking tools, and the Results describe AMELIE’s use of predefined phenotype and genotype-related features. The paper does not establish that phenotype-based approaches are limited in number or that none automatically learn features over patient phenotype terms; those broader claims are unsupported by this source. (LLM, conf=0.83)
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References 62 · Cited by 118
- Authors
- Birgmeier, J., Haeussler, M., Deisseroth, C. A., Steinberg, E. H., Jagadeesh, K. A., Ratner, A. J., Guturu, H., Wenger, A. M., Diekhans, M. E., Stenson, P. D., Cooper, D. N., Ré, C., Beggs, A. H., Bernstein, J. A., & Bejerano, G.
- Affiliations
- Stanford University (US)
- University of California, Santa Cruz (US)
- Stanford Medicine (US)
- Cardiff University (GB)
- Boston Children's Hospital (US)
- Corresponding author
- Bejerano, Gill
- ORCID
- Venue
- Science Translational Medicine
- Published
- 2020-05-20
- Volume
- 12
- Issue
- 544
- ISSN
- 1946-6234 (ISSN-L)
- 1946-6242
- PMC
- PMC9366928
- OpenAlex
- W3027582835
- Cited by
- 118
- References
- 62
- Citations by year
- 2026: 17 · 2025: 17 · 2024: 28 · 2023: 18 · 2022: 27 · 2021: 8 · 2020: 3
- FWCI
- 9.24
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- Science Translational Medicine (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Funders
- National Human Genome Research Institute (NHGRI U41HG002371-15)
- Stanford University
- Microsoft Research
- Defense Sciences Office, DARPA
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- Amélie, monogenic disorders, genetic diagnosis, literature mining, PubMed abstracts, genetic variants, candidate gene prioritization, rare diseases, singletons, causal gene identification
- MeSH terms
- Child, Genotype, Humans, Phenotype, Probability, Retrospective Studies, Exome
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