Genomics Proteomics Bioinformatics: AI-driven biomedical multimodal data fusion and analysis

Our team published a review in Genomics Proteomics Bioinformatics on the opportunities and challenges of AI-driven biomedical multimodal data fusion.

The review summarizes how deep learning and foundation-model style methods are being used to connect molecular, cellular, imaging, and electronic health record data for biomedical discovery and clinical translation.

Key points:

• Reviews multimodal biomedical data types spanning omics, imaging, and clinical records.
• Summarizes representation learning and fusion strategies used in modern biomedical AI.
• Highlights major open challenges in privacy, data fusion, and model interpretability.

Paper: Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis