Author

P. Bloomingdale

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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Cancer Biology· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Approach:machine learning (classical)· 2 Assay:imaging· 1 Approach:microscopy & imaging analysis· 1 Organism:mouse· 1 Assay:proteomics· 1 Organism:rat· 1

Frequent Co-Authors

A. N. Alexandrova· 1 shared C. V. Pastuskovas· 1 shared C. Y. Chabu· 1 shared D. De Alwis· 1 shared G. Ramey· 1 shared J. Distefano· 1 shared J. Soukup· 1 shared M. T. Cancilla· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperPharmacology & Toxicology

A. N. Alexandrova

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

A Light sheet fluorescence microscopy and machine learning-based approach to investigate drug and biomarker distribution in whole organs and tumors.

N. Kumar, P. Hrobar, M. Vagenknecht, J. Soukup, N. Patterson, P. Bloomingdale, T. Freshwater, S. Bardehle, R. Peter, R. Mangadu, C. V. Pastuskovas, C. Y. Chabu, M. T. Cancilla

This paper uses light sheet fluorescence microscopy and machine learning to study drug and biomarker distribution in whole organs and tumors, showing improved segmentation accuracy and subcellular resolution.

bioRxiv · Sep 16, 2023 · PreprintCancer Biology

An Artificial Intelligence Framework for Optimal Drug Design

G. Ramey, S. Vargas, D. De Alwis, A. N. Alexandrova, J. Distefano, P. Bloomingdale

This paper introduces an AI framework for optimal drug design that enhances brain exposure by integrating molecular design, QSAR, and PBPK modeling, showing significant improvements over existing drugs and random molecules.

bioRxiv · Oct 29, 2022 · PreprintPharmacology & Toxicology