Author

Y. T. Chong

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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Pharmacology & Toxicology· 2 Immunology· 1

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 3 Assay:imaging· 3 Approach:experimental assay· 2 Approach:deep learning· 1 Organism:human· 1

Frequent Co-Authors

E. Gustin· 2 shared H. Ceulemans· 2 shared J. K. Wegner· 2 shared A. Arany· 1 shared A. Carpenter· 1 shared A. E. Carpenter· 1 shared A. S. Low· 1 shared A. Vapirev· 1 shared

Activity by Year

2017-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersPharmacology & Toxicology

E. Gustin

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

M. F. Cuccarese, B. A. Earnshaw, K. Heiser, B. Fogelson, C. T. Davis, P. F. McLean, H. B. Gordon, K.-R. Skelly, F. L. Weathersby, V. Rodic, I. K. Quigley, E. D. Pastuzyn, B. M. Mendivil, N. H. Lazar, C. A. Brooks, , C. C. Gibson

This paper develops a deep-learning enabled phenomics platform to identify immune-modulating compounds and applies it to study SARS-CoV-2 infection and cytokine storm, identifying clinically relevant compounds and new drug candidates.

bioRxiv · Aug 2, 2020 · PreprintImmunology

Tales of 1,008 Small Molecules: Phenomic Profiling through Live-cell Imaging in a Panel of Reporter Cell Lines

M. J. Cox, S. Jaensch, J. Van de Waeter, L. Cougnaud, D. Seynaeve, S. Benalla, S. J. Koo, I. Van Den Wyngaert, J.-M. Neefs, D. Malkov, M. Bittremieux, M. Steemans, P. J. Peeters, J. K. Wegner, H. Ceulemans, , H. W. H. Göhlmann

The paper uses live-cell imaging of reporter cell lines to profile 1,008 compounds, identifying their mechanisms of action with high accuracy.

bioRxiv · Mar 13, 2020Pharmacology & Toxicology

Repurposed High-Throughput Images Enable Biological Activity Prediction For Drug Discovery

J. Simm, G. Klambauer, A. Arany, M. Steijaert, J. K. Wegner, E. Gustin, V. Chupakhin, Y. T. Chong, J. Vialard, P. Buijnsters, I. Velter, A. Vapirev, S. Singh, A. Carpenter, R. Wuyts, , H. Ceulemans

The paper repurposes high-throughput imaging data from a glucocorticoid receptor assay to predict activity in unrelated drug discovery assays, significantly increasing hit rates and chemical diversity.

bioRxiv · Mar 30, 2017Pharmacology & Toxicology