Guest Editor(s)
Ningbo Key Laboratory of Multi-Omics & Multimodal Biomedical Data Mining and Computing, Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, Zhejiang, China.
Special Issue Information
The convergence of artificial intelligence (AI), computational modeling, and biomedical data science is reshaping the landscape of modern biomedicine. Advances in high-throughput sequencing, multi-omics technologies, medical imaging, electronic health records, and digital health platforms have generated unprecedented volumes of biological and clinical data. Extracting meaningful knowledge from these heterogeneous datasets requires innovative computational approaches capable of uncovering complex biological patterns, predicting disease outcomes, and supporting personalized healthcare decisions.
Recent developments in machine learning, deep learning, big models, and explainable AI have significantly enhanced our ability to analyze biological systems across multiple scales, from molecules and cells to patients and populations. These advances are accelerating biomedical discovery, enabling more accurate disease diagnosis, facilitating therapeutic development, and promoting the realization of precision health.
This Special Issue, AI-Driven Computational Biomedicine: From Biological Data to Precision Health, aims to provide a platform for researchers to present novel computational methods, algorithms, and applications that advance biomedical research and healthcare. We welcome original research articles, reviews, and methodological studies that bridge biological data analysis and clinical translation, fostering interdisciplinary collaboration among computer scientists, bioinformaticians, biomedical researchers, and healthcare professionals.
Topics of interest include, but are not limited to: AI and machine learning for biomedical data analysis
• Multi-omics data integration and systems biology
• Computational genomics, transcriptomics, and proteomics
• Biomolecular sequence, structure, and function prediction
• Foundation models and large language models for biomedicine
• AI-assisted drug discovery and therapeutic development
• Drug-target interaction and molecular property prediction
• Medical image computing and radiomics
• Clinical informatics and electronic health record analytics
• Disease diagnosis, prognosis, and risk prediction
• Biomarker discovery and precision medicine
• Computational approaches for translational medicine
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