Author

A. J. Davis

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· 4 Ecology· 3 Animal Behavior & Cognition· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 5 Research Focus:cancer & tumor microenvironment· 3 Organism:human· 3 Assay:bulk RNA-seq· 2 Research Focus:cell type annotation· 2 Assay:DNA methylation· 2 Assay:imaging· 2 Research Focus:cell signaling & trafficking· 1

Frequent Co-Authors

J. Saha· 2 shared S.-Y. Wang· 2 shared A. Facoetti· 1 shared A. Pompos· 1 shared A. T. Gilbert· 1 shared A. Whitehurst· 1 shared B. J. Sishc· 1 shared B. Jump· 1 shared

Activity by Year

2020-2024 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 11 mutual co-author links

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2 shared papersCancer Biology

J. Saha

Mutual co-authors in cluster

Author profile →

8 papers

Recent·Most cited

BUB1 regulates non-homologous end joining pathway to mediate radioresistance in triple-negative breast cancer

S. Sriramulu, S. Thoidingjam, W.-M. Chen, O. Hassan, F. Siddiqui, S. L. Brown, B. Movsas, M. D. Green, A. J. Davis, C. Speers, E. Walker, S. Nyati

This study identifies BUB1 as a regulator of non-homologous end joining (NHEJ) in triple-negative breast cancer (TNBC), showing that its inhibition sensitizes TNBC cells to radiotherapy.

bioRxiv · May 7, 2024Cancer Biology

Defective Homologous Recombination and Genomic Instability Predict Increased Responsiveness to Carbon Ion Radiotherapy in Pancreatic Cancer

B. J. Sishc, J. Saha, E. Polsdofer, L. Ding, H. Lu, S.-Y. Wang, K. L. Swancutt, J. H. Nicholson, A. Facoetti, A. Pompos, M. Ciocca, T. A. Aguilera, M. D. Story, A. J. Davis

This study identifies PDAC cell lines defective in homologous recombination or with high genomic instability as more responsive to carbon ion radiotherapy, suggesting potential for targeted therapy.

bioRxiv · Apr 8, 2024Cancer Biology

Stable long-term individual variation in chimpanzee technological efficiency

S. Berdugo, E. Cohen, A. J. Davis, T. Matsuzawa, S. Carvalho

This study investigates long-term individual variation in chimpanzee nut-cracking efficiency over 25 years, finding stable differences that challenge previous sex-based conclusions.

bioRxiv · Nov 21, 2023 · PreprintAnimal Behavior & Cognition

The Cancer Testes Antigen, HORMAD1, is a Tumor-Specific Replication Fork Protection Factor

L. Herrera, K. McGlynn, Z. Gibbs, A. J. Davis, A. Whitehurst

This study identifies HORMAD1 as a tumor-specific factor that protects replication forks and prevents genomic instability in lung adenocarcinoma.

bioRxiv · Jan 31, 2023 · PreprintCancer Biology

Accounting for animal movement improves vaccination strategies against wildlife disease in heterogeneous landscapes

K. M. McClure, G. Bastille-Rousseau, A. J. Davis, C. A. Stengel, K. Nelson, R. B. Chipman, G. Wittemyer, Z. Abdo, A. T. Gilbert, K. M. Pepin

This study shows that habitat selection-based baiting strategies improve raccoon rabies seroprevalence in urban-suburban areas compared to current baiting methods.

bioRxiv · Oct 15, 2021Ecology

An efficient method of evaluating multiple concurrent management actions on invasive populations

A. J. Davis, R. O. Farrar, B. Jump, P. T. Hall, T. Guerrant, K. Pepin

The paper develops a method to assess the effectiveness of multiple concurrent management actions on invasive species using removal data, showing that removing 15% of the population monthly has a 95% chance of causing a population decline.

bioRxiv · Jul 28, 2021Ecology

Ablating Ku70 phosphorylation results in defective DNA damage repair and spontaneous induction of hepatocellular carcinoma

J. Saha, J. Bae, S.-y. Wang, L. J. Chappell, P. Gopal, A. J. Davis

This study shows that ablation of Ku70 phosphorylation leads to defective DNA repair and spontaneous hepatocellular carcinoma in mice.

bioRxiv · Mar 3, 2021Cancer Biology

Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2

M. A. Tabak, M. S. Norouzzadeh, D. W. Wolfson, E. J. Newton, R. K. Boughton, J. S. Ivan, E. A. Odell, E. S. Newkirk, R. Y. Conrey, J. Stenglein, F. Iannarilli, J. Erb, R. K. Brook, A. J. Davis, J. Lewis, , R. S. Miller

This paper develops two deep learning models for camera trap image classification, achieving high accuracy and transferability across locations, and provides an accessible R package for biologists.

bioRxiv · Mar 18, 2020Ecology