Combining Clinical, Genetic and Protein Markers Using Machine Learning Models Discriminates Psoriatic Arthritis Patients From Those With Psoriasis
Combining previously identified clinical, genetic and protein markers have a fair ability to differentiate PsA from PsC. Further studies are required for identifying better diagnostic signatures.
Authors
- Quan Li (ORCID: https://orcid.org/0000-0002-0565-9045)
- Lihi Eder (ORCID: https://orcid.org/0000-0002-1473-1715)
- A. Dohey
- D. Codner
- Max Kotlyar (ORCID: https://orcid.org/0000-0002-1111-8667)
- Dafna D. Gladman (ORCID: https://orcid.org/0000-0002-9074-0592)
- Darshini Ganatra (ORCID: https://orcid.org/0000-0002-3676-9124)
- Igor Jurišica (ORCID: https://orcid.org/0000-0002-2507-946X)
- Proton Rahman (ORCID: https://orcid.org/0000-0002-4521-2029)
- Fatima Abji (ORCID: https://orcid.org/0000-0001-9246-933X)
- Vinod Chandran (ORCID: https://orcid.org/0000-0002-8297-0275)
- Mozhgan Rasti
Institutions
- Memorial University of Newfoundland (CA)
- University of Toronto (CA)
- Women's College Hospital (CA)
- Krembil Research Institute
Publication Details
- Journal
- Journal of Psoriasis and Psoriatic Arthritis
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1177/24755303251344134
- Citations
- 1
- Primary Topic
- Psoriasis: Treatment and Pathogenesis
- Type
- article
- Field-Weighted Citation Impact
- 0.80
Funders
- National Psoriasis Foundation
- Canada Foundation for Innovation
- Krembil Foundation