Machine Learning in Precision Oncology: Innovations and Applications
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 4
Special Issue Editor
Interests: computational genomics; bioinformatics; medical image analysis; biological image analysis; statistical signal/image processing; computer vision; machine learning
Special Issue Information
Dear Colleagues,
Recent advances in machine learning and generative models have opened new fields of research in relation to precision oncology, enabling the development of highly personalized, data-driven approaches to cancer diagnosis, prognosis, and treatment. This Special Issue aims to highlight the latest research and innovations at the intersection of bioengineering, artificial intelligence, and oncology, with a focus on how machine learning and generative models—such as GANs, VAEs, diffusion models, and large language models—are reshaping cancer research and clinical workflows.
We invite contributions that highlight novel methodologies, translational research, and practical implementations of machine learning and generative AI in oncology. Topics of interest include—but are not limited to—cancer subtype classification, risk prediction models, treatment response forecasting, integration of multi-omics data, drug discovery, biomarker identification, AI-assisted pathology, and real-time decision support systems. Submissions showcasing interdisciplinary approaches that bridge machine learning, bioengineering, and clinical oncology are particularly encouraged.
This Special Issue aims to bring together researchers, clinicians, and engineers to present advances that push the boundaries of personalized cancer care through the application of intelligent systems.
Dr. Sheida Nabavi
Guest Editor
Manuscript Submission Information
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Keywords
- generative models in oncology
- machine learning in cancer research
- large language models in cancer
- diffusion models
- synthetic medical data
- AI-driven cancer diagnosis
- deep learning for oncology
- personalized cancer therapy
- cancer imaging and reconstruction
- predictive modeling in oncology
- data augmentation in medical imaging
- bioinformatics and generative AI
- multi-omics integration
- digital pathology and AI
- computational oncology
- clinical decision support systems
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