A clinical knowledge graph-based framework to prioritize candidate genes for facilitating diagnosis of Mendelian diseases and rare genetic conditions
Our findings demonstrate that PPAR, a method developed from the clinical knowledge graph, effectively ranks causal genes based on patient-derived HPO terms in rare and Mendelian disease contexts. PPAR has shown superior performance compared to other well-established HPO-only methods and provides an efficient, accessible solution for clinical geneticists. The Python-based tool is publicly available at https://github.com/dimi-lab/PPAR , offering a user-friendly platform for gene prioritization.
Authors
- Nicholas V.C. Chia
- Rohan Gnanaolivu (ORCID: https://orcid.org/0000-0001-6995-6340)
- Gavin R. Oliver (ORCID: https://orcid.org/0000-0002-9948-3799)
- Garrett Jenkinson (ORCID: https://orcid.org/0000-0003-2548-098X)
- Eric W. Klee (ORCID: https://orcid.org/0000-0003-2946-5795)
- Wenan Chen (ORCID: https://orcid.org/0000-0003-0379-2368)
- Chen Wang (ORCID: https://orcid.org/0000-0002-8000-6685)
- Emily Blake (ORCID: https://orcid.org/0009-0001-3625-2571)
Institutions
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1186/s12859-025-06096-2
- Citations
- 3
- Primary Topic
- Genomics and Rare Diseases
- Type
- article
- Field-Weighted Citation Impact
- 5.45
Funders
- Center for Individualized Medicine, Mayo Clinic