Author

D. M. Yang

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Immunology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Assay:imaging· 1 Organism:mouse· 1 Assay:proteomics· 1

Frequent Co-Authors

F. Wu· 2 shared A. Reuben· 1 shared C. Cheng· 1 shared C. Liang· 1 shared C. Tang· 1 shared C. Yee· 1 shared C. Zhu· 1 shared D. E. Gerber· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersImmunology

F. Wu

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Author profile →

2 papers

Recent·Most cited

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Y. Han, Y. Yang, Y. Tian, F. J. Fattah, M. S. v. Itzstein, M. Zhang, X. Kang, D. M. Yang, J. Liu, Y. Xue, C. Liang, I. Raman, C. Zhu, O. Xiao, Y. Hu, , T. Wang

This paper develops pMTnet-omni, an AI system predicting TCR-pMHC binding across species and MHC classes, achieving high accuracy and showing clinical utility in predicting immune-related adverse events.

bioRxiv · Dec 1, 2023 · PreprintImmunology

A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

R. Rong, H. Sheng, K. W. Jin, F. Wu, D. Luo, Z. Wen, C. Tang, D. M. Yang, L. Jia, M. Amgad, L. A. D. Cooper, Y. Xie, X. Zhan, S. Wang, G. Xiao

This study introduces HD-Yolo, a deep learning method for faster and more accurate nuclei segmentation and tumor microenvironment quantification in whole-slide images.

bioRxiv · Dec 8, 2022 · PreprintPathology