Author

A. Gitter

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 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Approach:machine learning (classical)· 2 Assay:proteomics· 2 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

F. M. Hoffmann· 2 shared G. E. Ananiev· 2 shared S. A. Wildman· 2 shared S. S. Ericksen· 2 shared A. Augustin· 1 shared A. Baker· 1 shared A. F. Voter· 1 shared A. Mehle· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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2 shared papersPharmacology & Toxicology

F. M. Hoffmann

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4 papers

Recent·Most cited

An approachable, flexible, and practical machine learning workshop for biologists

C. S. Magnano, F. Mu, R. S. Russ, M. Cvetkovic, D. Treu, A. Gitter

The ML4Bio workshop teaches biologists machine learning basics through a low-time, low-coding, and accessible approach, improving their confidence in applying ML in research.

bioRxiv · Feb 3, 2022Scientific Communication & Education

Alternative splicing liberates a cryptic cytoplasmic isoform of mitochondrial MECR that antagonizes influenza virus

S. F. Baker, H. Meistermann, M. Tzouros, A. Baker, S. Golling, J. S. Polster, M. P. Ledwith, A. Gitter, A. Augustin, H. Javanbakht, A. Mehle

The paper identifies a cryptic cytoplasmic isoform of MECR generated by alternative splicing that inhibits influenza virus replication by blocking viral RNP assembly.

bioRxiv · Nov 9, 2020Microbiology

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