Author

W. Mo

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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Cancer Biology· 2 Immunology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:deep learning· 2 Approach:statistical model· 2 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1

Frequent Co-Authors

A. Brock· 2 shared K. E. Johnson· 2 shared A. Feng· 1 shared A. L. Gardner· 1 shared A. M. Al'Khafaji· 1 shared A. M. Jarrett· 1 shared C. Fan· 1 shared C. Rui· 1 shared

Activity by Year

2019-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersCancer Biology

A. Brock

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Author profile →

4 papers

Recent·Most cited

A ZBP1 isoform blocks ZBP1-mediated cell death

Z.-Y. Cai, P. Wu, W. Mo, Z.-H. Yang

This paper identifies a ZBP1 isoform (ZBP1-S) that suppresses ZBP1-mediated cell death by competing for Z-NA binding, revealing alternative splicing as a regulatory mechanism for inflammation.

bioRxiv · Feb 2, 2024Immunology

Morphologic Classification and Automatic Diagnosis of Bacterial Vaginosisby Deep Neural Networks

Z. Wang, L. Zhang, Y. Wang, Z. Liu, H. Bai, W. Wu, W. Mo, R. An, J. Li, N. Li, P. Li, X. Zeng, C. Rui, C. Fan, L. Geng, , Y. Cao

This paper develops CNN models to automatically classify Nugent scores from vaginal microscopy images, achieving higher accuracy than human experts.

bioRxiv · May 20, 2020 · PreprintMicrobiology

Integrating multimodal data sets into a mathematical framework to describe and predict therapeutic resistance in cancer

K. E. Johnson, G. R. Howard, D. Morgan, E. Brenner, A. L. Gardner, R. E. Durrett, W. Mo, A. M. Al'Khafaji, E. Sontag, A. M. Jarrett, T. E. Yankeelov, A. Brock

This paper develops a framework integrating single-cell transcriptomic data with longitudinal treatment response data to predict drug resistance in breast cancer, achieving higher predictive accuracy with integrated data.

bioRxiv · Feb 11, 2020Cancer Biology

Cancer cell population growth kinetics at low densities deviate from the exponential growth model and suggest an Allee effect

K. E. Johnson, G. Howard, W. Mo, M. K. Strasser, E. A. B. F. Lima, S. Huang, A. Brock

This paper investigates cancer cell growth at low densities and finds that an Allee effect model better describes growth kinetics than exponential growth, suggesting cooperative behavior among cells.

bioRxiv · Mar 28, 2019Cancer Biology