Advanced machine learning framework for enhancing breast cancer diagnostics through transcriptomic profiling
The proposed ML framework enhances diagnostic accuracy and interpretability, demonstrating strong generalizability on an external dataset. These findings highlight its potential for precision oncology and personalized breast cancer diagnostics.
Authors
- Bagher Farhood (ORCID: https://orcid.org/0000-0003-2290-7220)
- K. Satyam Naidu (ORCID: https://orcid.org/0009-0007-2302-4901)
- Subbulakshmi Ganesan (ORCID: https://orcid.org/0000-0002-8345-116X)
- Anupam Yadav (ORCID: https://orcid.org/0000-0002-3137-2961)
- Girish Chandra Sharma
- Mohaned Adil (ORCID: https://orcid.org/0009-0000-9607-0684)
- Anvar Rakhmatullaev
- Zainab H. Athab (ORCID: https://orcid.org/0009-0007-3736-5205)
- Mohamed J. Saadh
- Radhwan Abdul Kareem
- Hanan Hassan Ahmed
- Hayder Naji Sameer
- Aman Shankhyan
- Ahmed Yaseen (ORCID: https://orcid.org/0009-0000-6526-5083)
Institutions
- Jain University (IN)
- Kashan University of Medical Sciences (IR)
- Thi Qar University (IQ)
- NIMS University (IN)
- Ahl Al Bayt University (IQ)
- University of Kerbala (IQ)
- Tashkent Pediatric Medical Institute (UZ)
- Al-Farahidi University
- Chitkara University (IN)
- GLA University (IN)
- Middle East University (JO)
Publication Details
- Journal
- Discover Oncology
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1007/s12672-025-02111-3
- Citations
- 13
- Primary Topic
- Gene expression and cancer classification
- Type
- article
- Field-Weighted Citation Impact
- 10.48