Using artificial intelligence (AI) to model clinical variant reporting for next generation sequencing (NGS) oncology assays
Longitudinally acquired NGS assay data provide a strong basis for machine learning models for decision support to select variants for clinical oncology reports. The models provide a framework for consistent reporting practices and reducing inter-reviewer variability. To improve model transparency, individual variant predictions are able to be presented as part of reviewer workflows.
Authors
- Rashindrie Perera (ORCID: https://orcid.org/0000-0001-6822-2900)
- Andrew Fellowes (ORCID: https://orcid.org/0000-0003-4267-3599)
- Yamuna Kankanige (ORCID: https://orcid.org/0000-0001-9108-4083)
- Richard Lupat (ORCID: https://orcid.org/0000-0002-6435-7100)
- Kenneth Doig (ORCID: https://orcid.org/0000-0003-0677-9214)
- Ella R. Thompson (ORCID: https://orcid.org/0000-0001-5206-5111)
- Piers A. Blombery (ORCID: https://orcid.org/0000-0002-9902-0022)
- Stephen B. Fox (ORCID: https://orcid.org/0000-0002-7648-8896)
- Jason Li (ORCID: https://orcid.org/0000-0002-1150-3549)
Institutions
- The University of Melbourne (AU)
- Peter MacCallum Cancer Centre (AU)
Publication Details
- Journal
- BioData Mining
- Published
- 2025-10-29
- DOI
- https://doi.org/10.1186/s13040-025-00489-y
- Citations
- 3
- Primary Topic
- Cancer Genomics and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 2.81
Funders
- Australian Government
- Medical Research Council
- National Health and Medical Research Council