Author

X.-h. Shi

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· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1 Organism:human· 1 Assay:imaging· 1 Assay:proteomics· 1

Frequent Co-Authors

B. Wang· 1 shared B.-x. Ke· 1 shared C. Wu· 1 shared C. Xu· 1 shared C.-g. Jiang· 1 shared C.-p. Lv· 1 shared C.-w. Ke· 1 shared D.-q. Liu· 1 shared

Activity by Year

2020-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperImmunology

B. Wang

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2 papers

Recent·Most cited

The immunodominant and neutralization linear epitopes for SARS-CoV-2

S. Lu, X.-x. Xie, L. Zhao, B. Wang, J. Zhu, T.-r. Yang, G.-w. Yang, M. Ji, C.-p. Lv, J. Xue, E.-h. Dai, X.-m. Fu, D.-q. Liu, L. Zhang, S.-j. Hou, , R.-t. Liu

This study identifies immunodominant and neutralizing epitopes on SARS-CoV-2 proteins through computational simulation and experimental validation in mice.

bioRxiv · Aug 27, 2020 · PreprintImmunology

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

G. Yu, T. Xie, C. Xu, X.-H. Shi, C. Wu, K. Sun, R.-Q. Meng, X.-H. Meng, K.-S. Wang, H.-M. Xiao, H.-W. Deng

This paper develops a semi-supervised deep learning model for colorectal cancer recognition using pathological images, showing it outperforms supervised learning with limited labeled data and matches pathologists' performance.

bioRxiv · Jul 13, 2020Pathology