Wang, Sheng

The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans

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Single-cell and spatial transcriptomicsGenomics and Chromatin Dynamics

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A whole-slide foundation model for digital pathology from real-world data

Xu, H., Usuyama, N., Bagga, J., Zhang, S., Rao, R., Naumann, T., Wong, C., Gero, Z., González, J., Gu, Y., et al. (2024) · Nature

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

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A Multimodal Biomedical Foundation Model Trained from Fifteen Million Image–Text Pairs

Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., et al. (2025) · NEJM AI

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

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Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures

Luo, S., Su, Y., Peng, X., Wang, S., Peng, J., & Ma, J. (2022) · neural information processing systems

vaccines and immunoinformatics approachesMonoclonal and Polyclonal Antibodies Research

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MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

Zhao, H., Cai, Z., Si, S., Ma, X., An, K., Chen, L., Liu, Z., Wang, S., Han, W., & Chang, B. (2023) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

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BiomedParse: a biomedical foundation model for image parsing of everything everywhere all at once

Zhao, T., Gu, Y., Yang, J., Usuyama, N., Lee, H. H., Naumann, T., Gao, J., Crabtree, A., Abel, J., Moung-Wen, C., et al. (2024) · arXiv

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

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Cell types of origin of the cell-free transcriptome

Vorperian, S. K., Moufarrej, M. N., Tabula Sapiens Consortium, Overall Project Direction and Coordination, Jones, R. C., Karkanias, J., Krasnow, M., Pisco, A. O., Quake, S. R., Salzman, J., et al. (2022) · Nature Biotechnology

Single-cell and spatial transcriptomicsGene expression and cancer classification

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Opportunities and challenges for the computational interpretation of rare variation in clinically important genes

McInnes, G., Sharo, A. G., Koleske, M. L., Brown, J. E. H., Norstad, M., Adhikari, A. N., Wang, S., Brenner, S. E., Halpern, J., Koenig, B. A., et al. (2021) · The American Journal of Human Genetics

Genomics and Rare DiseasesArtificial Intelligence in Healthcare and Education

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A clinically accessible small multimodal radiology model and evaluation metric for chest X-ray findings

Zambrano Chaves, J. M., Huang, S.-C., Xu, Y., Xu, H., Usuyama, N., Zhang, S., Wang, F., Xie, Y., Khademi, M., Yang, Z., et al. (2025) · Nature Communications

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

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