Author

M. S. Wong

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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Biophysics· 1 Cell Biology· 1 Evolutionary Biology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Research Focus:chromatin & epigenetic regulation· 1 Assay:DNA methylation· 1

Frequent Co-Authors

A. R. Krainer· 2 shared D. M. McCandlish· 2 shared J. B. Kinney· 2 shared A. Ayaz· 1 shared A. J. George· 1 shared C. Engel· 1 shared C. M. Gould· 1 shared C. Marti-Gomez· 1 shared

Activity by Year

2020-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBiophysics

A. R. Krainer

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Specificity, synergy, and mechanisms of splice-modifying drugs

Y. Ishigami, M. S. Wong, C. Marti-Gomez, A. Ayaz, M. Kooshkbaghi, S. Hanson, D. M. McCandlish, A. R. Krainer, J. B. Kinney

This study develops quantitative models to understand how splice-modifying drugs work, showing drug-specific mechanisms and revealing widespread drug synergy and cooperativity.

bioRxiv · Dec 30, 2022Biophysics

Nuclear stabilisation of p53 requires a functional nucleolar surveillance pathway

K. M. Hannan, P. Soo, M. S. Wong, J. K. Lee, N. Hein, M. Evers, K. D. Wysoke, T. D. Williams, C. Montellese, L. Smith, S. J. Al-Obaidi, L. Nunez-Villacis, P. Poh, M. Pavy, J.-S. He, , A. J. George

This study identifies the nucleolar surveillance pathway (NSP) as a central regulator of p53 stability, showing its role in integrating various cellular stresses and its potential as a therapeutic target in cancer.

bioRxiv · Jan 21, 2021 · PreprintCell Biology

Higher-order epistasis and phenotypic prediction

J. Zhou, M. S. Wong, W.-C. Chen, A. R. Krainer, J. B. Kinney, D. M. McCandlish

This paper introduces a method to model higher-order genetic interactions from partially observed data and applies it to predict phenotypes in antibody-binding domains and splicing efficiency.

bioRxiv · Oct 14, 2020Evolutionary Biology