Author

Kaitlyn Gayvert

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

Signature Assays, Organisms & Methods

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Research Focus:drug response / drug discovery· 2 Approach:machine learning (classical)· 2

Frequent Co-Authors

Olivier ElementoiD· 2 shared Coryandar Gilvary· 1 shared Giuseppe Galletti· 1 shared Joshua E. Allen· 1 shared Linda Huang· 1 shared Martin Stogniew· 1 shared Neel S. MadhukariD· 1 shared Neel S. Madhukar· 1 shared

Activity by Year

2017-2018 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 31 mutual co-author links

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2 shared papersPharmacology & Toxicology

Olivier ElementoORCID

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2 papers

Recent·Most cited

A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events

Neel S. Madhukar, Kaitlyn Gayvert, Coryandar Gilvary, Olivier Elemento

MAESTER predicts tissue-specific drug adverse events by integrating compound structure, targets, phenotypic effects, and genomic data, achieving high accuracy and identifying previously unknown adverse events.

bioRxiv · Mar 24, 2018 · PreprintPharmacology & Toxicology

A New Big-Data Paradigm For Target Identification And Drug Discovery

Neel S. Madhukar, Prashant K. Khade, Linda Huang, Kaitlyn Gayvert, Giuseppe Galletti, Martin Stogniew, Joshua E. Allen, Paraskevi Giannakakou, Olivier Elemento

This paper introduces BANDIT, a Bayesian machine-learning framework for drug target identification, achieving 90% accuracy in predicting targets for 2000 small molecules and validating novel microtubule inhibitors and the target of ONC201.

bioRxiv · May 7, 2017 · PreprintPharmacology & Toxicology