Author

H. Green

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 Cancer Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Assay:bulk RNA-seq· 1 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:cell type annotation· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

A. Gazzo· 1 shared A. L. F. Longhini· 1 shared B. D. Greenbaum· 1 shared B. Weigelt· 1 shared D. R. Koes· 1 shared D. Zamarin· 1 shared E. De Stanchina· 1 shared H. Dopeso· 1 shared

Activity by Year

2021-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperCancer Biology

A. Gazzo

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Interferon response and epigenetic modulation by SMARCA4 mutations drive ovarian tumor immunogenicity

M. N. Brodeur, H. Dopeso, Y. Zhu, A. L. F. Longhini, A. Gazzo, S. Sun, R. Koche, R. Qu, P.-J. Hamard, Y. Bykov, H. Green, K. B. Chiappinelli, M. A. Ozsoy, T. Basili, R. Gardner, , D. Zamarin

This study shows that SMARCA4 mutations in ovarian cancer increase immunogenicity through interferon response and epigenetic changes, enhancing T cell and NK cell activity in the tumor microenvironment.

bioRxiv · Aug 8, 2023 · PreprintCancer Biology

DeepFrag: An Open-Source Browser App for Deep-Learning Lead Optimization

H. Green, J. Durrant

DeepFrag provides a browser-based app for lead optimization using deep learning, eliminating the need for coding or server uploads.

Journal of Chemical Information and Modeling · May 24, 2021Peer-reviewedPharmacology & Toxicology

DeepFrag: A Deep Convolutional Neural Network for Fragment-based Lead Optimization

H. Green, D. R. Koes, J. D. Durrant

DeepFrag predicts chemical fragments to improve ligand binding affinity using a deep convolutional neural network, achieving 58% accuracy in selecting correct fragments from a large set.

Chemical Science · Jan 1, 2021Peer-reviewedPharmacology & Toxicology