Author

D. P. Walsh

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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Ecology· 3

Signature Assays, Organisms & Methods

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Research Focus:host-pathogen & immune response· 2 Organism:mouse· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Organism:human· 1

Frequent Co-Authors

A. J. Davis· 1 shared B. A. Mosher· 1 shared D. J. Storm· 1 shared D. R. Stahler· 1 shared D. W. Wolfson· 1 shared E. A. Odell· 1 shared E. E. Brandell· 1 shared E. G. Rosenblatt· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 12 mutual co-author links

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1 shared paperEcology

A. J. Davis

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Epidemiological modeling of SARS-CoV-2 in white-tailed deer (Odocoileus virginianus) reveals conditions for introduction and widespread transmission

E. G. Rosenblatt, J. D. Cook, G. V. DiRenzo, E. H. C. Grant, F. Arce, K. M. Pepin, F. J. Rudolph, M. C. Runge, S. Shriner, D. P. Walsh, B. A. Mosher

This study uses epidemiological modeling to assess SARS-CoV-2 transmission risks in white-tailed deer, showing higher risk in captive populations and potential for widespread outbreaks.

bioRxiv · Aug 30, 2023 · PreprintEcology

Detection of prions from spiked and free-ranging carnivore feces

H. N. Inzalaco, E. E. Brandell, S. P. Wilson, M. Hunsaker, D. R. Stahler, K. Woelfel, D. P. Walsh, T. Nordeen, D. J. Storm, S. S. Lichtenberg, W. C. Turner

This study detects prions in carnivore feces using RT-QuIC, showing that prions are detectable in both spiked and free-ranging samples, reflecting cervid population prevalence.

bioRxiv · Jul 31, 2023Ecology

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