Author

M. A. Newton

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· 1 Immunology· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:clustering· 1 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

A. Gitter· 1 shared B. Deng· 1 shared C. L. Zuleger· 1 shared C. Zhou· 1 shared C.-p. Lee· 1 shared D. Lee· 1 shared F. M. Hoffmann· 1 shared G. E. Ananiev· 1 shared

Activity by Year

2019-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 16 mutual co-author links

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1 shared paperPharmacology & Toxicology

A. Gitter

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Author profile →

3 papers

Recent·Most cited

Intermittent energy restriction inhibits tumor growth and enhances paclitaxel response in a transgenic mouse model of endometrial cancer

Z. Zhao, J. Wang, W. Kong, Z. Fang, M. Coleman, G. Milne, W. C. Burkett, M. A. Newton, D. Lee, B. Deng, X. Shen, H. Suo, W. Sun, S. Hursting, C. Zhou, V. L. Bae-Jump

This study shows that intermittent energy restriction (IER) reduces tumor growth and improves paclitaxel response in a mouse model of endometrial cancer compared to low-fat diet.

bioRxiv · Feb 2, 2024Cancer Biology

Surrogate selection oversamples expanded T cell clonotypes

P. Yu, Y. Lian, C. L. Zuleger, R. J. Albertini, M. R. Albertini, M. A. Newton

The paper develops a model to quantify how surrogate selection assays may enrich for amplified T cell clones and explores its implications for immunological data analysis.

bioRxiv · Jul 13, 2023Immunology

Predicting kinase inhibitors using bioactivity matrix derived informer sets

H. Zhang, S. S. Ericksen, C.-p. Lee, G. E. Ananiev, N. Wlodarchak, J. C. Mitchell, A. Gitter, S. J. Wright, F. M. Hoffmann, S. A. Wildman, M. A. Newton

The paper develops an informer-based ranking method to predict kinase inhibitor activity using chemogenomic data, outperforming traditional chemical diversity-based approaches in both retrospective and prospective tests.

bioRxiv · Jan 28, 2019Pharmacology & Toxicology