Phrank measures phenotype sets similarity to greatly improve Mendelian diagnostic disease prioritization
Open accessPublished version of a preprint
Abstract
Purpose Exome sequencing and diagnosis is beginning to spread across the medical establishment. The most time-consuming part of genome-based diagnosis is the manual step of matching the potentially long list of patient candidate genes to patient phenotypes to identify the causative disease.Methods We introduce Phrank (for phenotype ranking), an information theory-inspired method that utilizes a Bayesian network to prioritize candidate diseases or genes, as a stand-alone module that can be run with any underlying knowledgebase and any variant filtering scheme.Results Phrank outperforms existing methods at ranking the causative disease or gene when applied to 169 real patient exomes with Mendelian diagnoses. Phrank's greatest improvement is in disease space, where across all 169 patients it ranks only 3 diseases on average ahead of the true diagnosis, whereas Phenomizer ranks 32 diseases ahead of the causal one.Conclusions Using Phrank to rank all patient candidate genes or diseases, as they start working through a new case, will save the busy clinician much time in deriving a genetic diagnosis.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Leveraging indirect associations is essential for diagnosing patients with novel or atypical genetic conditions.
“leveraging indirect associations is essential for diagnosing patients with novel or atypical genetic conditions”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [16]
The Methods section on Phrank inputs (p. 3) describes using ontology relationships to infer broader phenotype associations from specific ones, supporting the idea of leveraging indirect associations for diagnostic ranking. But the paper does not establish that doing so is essential for diagnosing novel or atypical conditions; its benchmark section (p. 4) says cases proposing novel causative genes were excluded. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe information-theoretic approach Phrank can be leveraged to calculate the semantic similarity between two sets of patient phenotype terms based on the information content of their shared phenotype ancestors in the Human Phenotype Ontology.
“The information-theoretic approach Phrank can be leveraged to calculate the semantic similarity between two sets of patient phenotype terms based on the information content of their shared phenotype ancestors in the Human Phenotype Ontology16.”
From Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases (2025) · cites this paper as [16]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 44 · Cited by 56
- Authors
- Jagadeesh, K. A., Birgmeier, J., Guturu, H., Deisseroth, C. A., Wenger, A. M., Bernstein, J. A., & Bejerano, G.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Corresponding author
- Bejerano, Gill
- ORCID
- Venue
- Genetics in Medicine
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 21
- Issue
- 2
- Pages
- 464-470
- ISSN
- 1098-3600 (ISSN-L)
- OpenAlex
- W2951752341
- Cited by
- 56
- References
- 44
- Citations by year
- 2026: 8 · 2025: 8 · 2024: 7 · 2023: 6 · 2022: 12 · 2021: 3 · 2020: 4 · 2019: 6 · 2018: 2
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Genetics in Medicine (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (GEN/284/12, WT 098051, 10/H0305/83)
- National Institute for Health and Care Research (10/H0305/83, WT098051)
- Defense Advanced Research Projects Agency
- Center for Computational, Evolutionary and Human Genomics, Stanford University
- 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 › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- candidate gene prioritization, phenotype similarity, Bayesian networks, information theory, exome sequencing, genetic diagnosis, variant filtering
- MeSH terms
- Diagnosis, Computer-Assisted, Genetic Testing, Humans, Phenotype, Software, Computational Biology, Benchmarking, Genetic Diseases, Inborn, Knowledge Bases, Pathology, Molecular, Exome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
