Author

S. S. Ericksen

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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Biochemistry· 1 Microbiology· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:drug response / drug discovery· 3 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1 Approach:statistical model· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

A. Gitter· 2 shared F. M. Hoffmann· 2 shared G. E. Ananiev· 2 shared S. A. Wildman· 2 shared A. F. Voter· 1 shared C. R. Henthorn· 1 shared C.-p. Lee· 1 shared H. Zhang· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 26 mutual co-author links

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2 shared papersPharmacology & Toxicology

A. Gitter

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Author profile →

3 papers

Recent·Most cited

MicrobiologybioRxivPreprintJul 25, 2022

Multivariate chemogenomic screening prioritizes new macrofilaricidal leads

N. J. Wheeler, K. T. Ryan, K. J. Gallo…·M. Zamanian

This study uses a multivariate screen to identify macrofilaricidal compounds, finding 17 compounds with strong effects on adult filariae and five with high potency against adults but low potency against microfilariae.

DOI 10.1101/2022.07.25.501423

deep learningdrug response / drug discovery

𝕏 PostBluesky

Pharmacology & ToxicologybioRxivJan 28, 2019

Predicting kinase inhibitors using bioactivity matrix derived informer sets

H. Zhang, S. S. Ericksen, C.-p. Lee…·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.

DOI 10.1371/journal.pcbi.1006813

proteomicsdeep learning

𝕏 PostBluesky

BiochemistrybioRxivSep 15, 2018

Practical model selection for prospective virtual screening

S. Liu, M. Alnammi, S. S. Ericksen…·A. Gitter

The paper evaluates virtual screening algorithms for two protein-protein interactions and finds random forest outperforms neural networks in prospective compound prioritization.

DOI 10.1021/acs.jcim.8b00363

machine learning (classical)deep learning

𝕏 PostBluesky