Machine Learning in Biomedical Engineering
A special issue of Biology (ISSN 2079-7737). This special issue belongs to the section "Bioinformatics".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 243
Special Issue Editor
Interests: bioinformatics; genetics; genomics; machine learning; ceRNA network; predictive modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning has revolutionized the field of biomedical engineering. Traditional biomedical engineering, such as digital human models, focuses on accurately measuring 3D structures. However, with the advent of high-throughput technologies, biomedical big data are now being generated on an unprecedented scale. AI models can be trained using these big data, enabling new research possibilities such as digital twins. These digital twins can replicate not only the physical characteristics of humans or mice, as seen in traditional digital models, but also their behavior, driven by AI models utilizing big data. This enables predictions on reactions to drugs or exposures, aiding in testing new cancer treatments. They have evolved from mere 3D images to valuable tools replacing costly or infeasible experiments.
I am pleased to invite you to contribute your work to our Special Issue dedicated to showcasing the latest advancements in machine learning in biomedical engineering. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Novel high-throughput technologies for biomedical engineering;
- Databases for biomedical big data;
- Machine learning methods for integrating multi-omics data;
- AI models for analyzing biomedical big data;
- Virtual cell simulations based on multimodal data;
- Virtual mouse simulations based on multimodal data;
- Digital twins with large language models;
- Digital twins for cancer patients;
- AI-enhanced drug design and development.
I look forward to receiving your contributions.
Dr. Tao Huang
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- biomedical engineering
- digital twin
- big data
- cancer
- drug
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