Author

S. Yip

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 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Research Focus:cell-cell communication· 2 Research Focus:differential expression· 2 Organism:human· 2 Approach:biophysical modeling· 1 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:clustering· 1

Frequent Co-Authors

A. Gagliardi· 1 shared A. J. Schaumberg· 1 shared A. Moradian· 1 shared A. Morini· 1 shared B. D. Sener· 1 shared B. S. Pritt· 1 shared B. Xu· 1 shared C. Miguel· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperCancer Biology

A. Gagliardi

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Author profile →

3 papers

Recent·Most cited

Multi-omic analysis of CIC's functional networks reveals novel interaction partners and a potential role in mitotic fidelity

Y. Takemon, V. G. LeBlanc, J. Song, S. Y. Chan, S. D. Lee, D. L. Trinh, S. T. Ahmad, W. R. Brothers, R. D. Corbett, A. Gagliardi, A. Moradian, J. G. Cairncross, S. Yip, S. A. Aparicio, J. A. Chan, , M. A. Marra

This study uses multi-omic approaches to analyze CIC's functional networks, identifying novel interactions and revealing its role in mitotic fidelity and transcriptional splicing.

bioRxiv · Apr 6, 2023Cancer Biology

ATM-deficient lung, prostate and pancreatic cancer cells are acutely sensitive to the combination of olaparib and the ATR inhibitor AZD6738

N. R. Jette, S. Radhamani, R. Ye, Y. Yu, G. Arthur, S. Goutam, T. Bizmar, M. Kumar, P. Bose, S. Yip, M. Kolinsky, S. P. Lees-MIller

This study shows that ATM-deficient cancer cells are more sensitive to the combination of olaparib and AZD6738, leading to cell death.

bioRxiv · Mar 13, 2020Cancer Biology

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media

A. J. Schaumberg, W. C. Juarez-Nicanor, S. J. Choudhury, L. G. Pastrian, B. S. Pritt, M. Prieto Pozuelo, R. Sotillo Sanchez, K. Ho, N. Zahra, B. D. Sener, S. Yip, B. Xu, S. R. Annavarapu, A. Morini, K. A. Jones, , T. J. Fuchs

The paper develops interpretable deep learning models to search for similar disease states across social media and PubMed using histopathology images and clinical data, achieving high precision in real-time diagnosis assistance.

bioRxiv · Mar 9, 2020Pathology