A multilevel ensemble model for predicting mutation types in bladder cancer
This novel study highlights the potential of AI techniques for early detection of mutations in urinary bladder cancer. The highly accurate prediction capability of the developed model offers promise for identifying diagnostic and prognostic mutational landscapes to guide future therapeutic strategies.
Authors
- Prashant Singh Rana (ORCID: https://orcid.org/0000-0002-0142-7925)
- Harpreet Singh (ORCID: https://orcid.org/0000-0002-2365-538X)
- Divisha Garg (ORCID: https://orcid.org/0000-0001-5550-2911)
- Akhil Jain (ORCID: https://orcid.org/0000-0001-7298-4246)
- Sumit Dey (ORCID: https://orcid.org/0000-0001-7331-6661)
- Smita Pattanaik
- Ravimohan Suryanarayan Mavuduru
Institutions
- Thapar Institute of Engineering & Technology (IN)
- Institute of Post Graduate Medical Education and Research (IN)
- Post Graduate Institute of Medical Education and Research (IN)
- Virginia Tech (US)
Publication Details
- Journal
- Personalized Medicine
- Published
- 2025-11-02
- DOI
- https://doi.org/10.1080/17410541.2025.2592530
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 0.00