Author

F. M. Hoffmann

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 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 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 G. E. Ananiev· 2 shared S. A. Wildman· 2 shared S. S. Ericksen· 2 shared A. F. Voter· 1 shared C.-p. Lee· 1 shared H. Zhang· 1 shared J. C. Mitchell· 1 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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2 shared papersPharmacology & Toxicology

A. Gitter

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2 papers

Recent·Most cited

Predicting kinase inhibitors using bioactivity matrix derived informer sets

H. Zhang, S. S. Ericksen, C.-p. Lee, G. E. Ananiev, N. Wlodarchak, J. C. Mitchell, A. Gitter, S. J. Wright, F. M. Hoffmann, S. A. Wildman, 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.

bioRxiv · Jan 28, 2019Pharmacology & Toxicology

Practical model selection for prospective virtual screening

S. Liu, M. Alnammi, S. S. Ericksen, A. F. Voter, G. E. Ananiev, J. L. Keck, F. M. Hoffmann, S. A. Wildman, A. Gitter

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

bioRxiv · Sep 15, 2018Biochemistry