From Anatomy to Genomics Using a Multi-Task Deep Learning Approach for Comprehensive Glioma Profiling
MGMT-Net presents a scalable and clinically relevant solution that bridges radiological imaging and genomic insights, potentially reducing diagnostic latency and enhancing precision in neuro-oncology decision-making. By integrating spatial and genetic analysis within a single model, this work represents a significant step toward comprehensive, AI-driven glioma assessment.
Authors
- Sabina Umirzakova (ORCID: https://orcid.org/0000-0002-7694-1806)
- Abror Shavkatovich Buriboev (ORCID: https://orcid.org/0000-0001-8024-6200)
- Akhram Nishanov (ORCID: https://orcid.org/0000-0002-5652-8977)
- Akmalbek Bobomirzaevich Abdusalomov (ORCID: https://orcid.org/0000-0001-5923-8695)
- Ryum-Duck Oh
- Alpamis Kutlimuratov (ORCID: https://orcid.org/0000-0001-7472-4165)
- Adilbek Dauletov
- Obidjon Bekmirzaev
Institutions
- Korea National University of Transportation (KR)
- Gachon University (KR)
- Tashkent University of Information Technology (UZ)
- Tashkent State Technical University named after Islam Karimov (UZ)
- Tashkent State University of Economics (UZ)
- Westminster International University in Tashkent (UZ)
Publication Details
- Journal
- Bioengineering
- Published
- 2025-09-15
- DOI
- https://doi.org/10.3390/bioengineering12090979
- Citations
- 1
- Primary Topic
- Advanced Neural Network Applications
- Type
- article
- Field-Weighted Citation Impact
- 1.28