Author

E. Jinks

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 Cell Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Assay:imaging· 3 Organism:zebrafish· 3 Research Focus:cell type annotation· 2 Research Focus:ecology & biodiversity· 2 Approach:experimental assay· 1 Organism:human· 1 Assay:proteomics· 1

Frequent Co-Authors

E. Ditria· 3 shared R. Connolly· 3 shared S. Lopez-Marcano· 3 shared M. Sievers· 2 shared A. Olds· 1 shared A. S. Lavado· 1 shared A. Teodosio· 1 shared C. A. Brady· 1 shared

Activity by Year

2019-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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3 shared papersEcology

E. Ditria

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

A cell-based, spike protein binding assay highlights differences in antibody neutralising capacity for SARS-CoV-2 variants

N. Harrison, L. Richardson, C. Pallini, I. Morano, E. Jinks, J. Cowley, H. Chan, H. Hill, C. Matas de las Heras, A. Teodosio, A. S. Lavado, T. R. Dafforn, D. K. Grammatopoulos, J. Gordon, C. A. Brady, , O. S. Qureshi

The paper develops a cell-based ACE2/spike protein binding assay to assess antibody neutralising capacity against SARS-CoV-2 variants and shows differential blocking activity of monoclonal antibodies.

bioRxiv · Jun 24, 2022 · PreprintCell Biology

Improved accuracy for automated counting of a fish in baited underwater videos for stock assessment

R. Connolly, D. Fairclough, E. Jinks, E. Ditria, G. Jackson, S. Lopez-Marcano, A. Olds, K. Jinks

This paper improves automated counting of fish in BRUVS videos by optimizing confidence thresholds, Seq-NMS, and correction equations to achieve high accuracy across fish abundances.

bioRxiv · Feb 1, 2021 · PreprintEcology

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

E. Ditria, M. Sievers, R. Connolly, E. Jinks, S. Lopez-Marcano

This study trains deep learning models to detect luderick fish in coastal habitats, finding that models trained on multiple habitats perform better across different environments.

bioRxiv · May 19, 2020Ecology

Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

E. Ditria, S. Lopez-Marcano, E. Jinks, M. Sievers, C. J. Brown, R. Connolly

This study demonstrates that deep learning outperforms human experts and citizen scientists in accurately and efficiently quantifying fish abundance from underwater footage.

bioRxiv · Oct 16, 2019Ecology