InpherNet accelerates monogenic disease diagnosis using patients’ candidate genes’ neighbors
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Abstract
Purpose Roughly 70% of suspected Mendelian disease patients remain undiagnosed after genome sequencing, partly because knowledge about pathogenic genes is incomplete and constantly growing. Generating a novel pathogenic gene hypothesis from patient data can be time-consuming especially where cohort-based analysis is not available.Methods Each patient genome contains dozens to hundreds of candidate variants. Many sources of indirect evidence about each candidate may be considered. We introduce InpherNet, a network-based machine learning approach leveraging Monarch Initiative data to accelerate this process.Results InpherNet ranks candidate genes based on orthologs, paralogs, functional pathway members, and colocalized interaction partner gene neighbors. It can propose novel pathogenic genes and reveal known pathogenic genes whose diagnosed patient-based annotation is missing or partial. InpherNet is applied to patient cases where the causative gene is incorrectly ranked low by clinical gene-ranking methods that use only patient-derived evidence. InpherNet correctly ranks the causative gene top 1 or top 1-5 in roughly twice as many cases as seven comparable tools, including in cases where no clinical evidence for the diagnostic gene is in our knowledgebase.Conclusion InpherNet improves the state of the art in considering candidate gene neighbors to accelerate monogenic 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 [27]
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Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
Alsentzer, E., Li, M. M., Kobren, S. N., Noori, A., Undiagnosed Diseases Network, Kohane, I. S., & Zitnik, M. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01749-1 · Cited by 54 · see citing works
- Authors
- Yoo, B., Birgmeier, J., Bernstein, J. A., & Bejerano, G.
- Affiliations
- Stanford University (US)
- Stanford Medicine (US)
- Corresponding author
- Bejerano, Gill
- Venue
- Genetics in Medicine
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 23
- Issue
- 10
- Pages
- 1984-1992
- ISSN
- 1098-3600 (ISSN-L)
- OpenAlex
- W3181227402
- Cited by
- 4
- References
- 46
- Citations by year
- 2026: 1 · 2025: 2 · 2022: 1
- FWCI
- 0.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Genetics in Medicine (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- School of Medicine, Stanford University
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- 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
- Keywords
- monogenic disorders, candidate gene prioritization, gene network analysis, orthologs and paralogs, pathway enrichment, Monarch Initiative, undiagnosed patients, genome sequencing
- MeSH terms
- Machine Learning, Humans, Cohort Studies, Genetic Diseases, Inborn, Knowledge Bases
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