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Michael Bronstein

Showing results (1-10 of 23) with videos related to

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Chemical Science|April 13, 2026
Black-box data: a new paradigm for biomedicine in the AI eraLuca Naef, Michael Bronstein
IEEE Transactions on Visualization and Computer Graphics|July 12, 2007
Calculus of nonrigid surfaces for geometry and texture manipulationAlexander Bronstein, Michael Bronstein, Ron Kimmel
Clinical Pharmacology and Therapeutics|January 3, 2024
The Future of Machine Learning Within Target Identification: Causality, Reversibility, and DruggabilityJake P Taylor-King, Michael Bronstein, David Roblin
Human Genomics|June 8, 2021
Predicting anticancer hyperfoods with graph convolutional networksGuadalupe Gonzalez, Shunwang Gong, Ivan Laponogov, et al.
Nature Biomedical Engineering|September 9, 2025
Combinatorial prediction of therapeutic perturbations using causally inspired neural networksGuadalupe Gonzalez, Xiang Lin, Isuru Herath, et al.
Biorxiv : the Preprint Server for Biology|January 23, 2024
Combinatorial prediction of therapeutic perturbations using causally-inspired neural networksGuadalupe Gonzalez, Xiang Lin, Isuru Herath, et al.
Cell Systems|November 16, 2023
A new age in protein design empowered by deep learningHamed Khakzad, Ilia Igashov, Arne Schneuing, et al.
Science (New York, N.Y.)|July 13, 2023
Using machine learning to decode animal communicationChristian Rutz, Michael Bronstein, Aza Raskin, et al.
Nature Communications|January 26, 2022
Interaction data are identifiable even across long periods of timeAna-Maria Creţu, Federico Monti, Stefano Marrone, et al.
Scientific Reports|July 5, 2019
HyperFoods: Machine intelligent mapping of cancer-beating molecules in foodsKirill Veselkov, Guadalupe Gonzalez, Shahad Aljifri, et al.
Pageof 3

Showing results (1-10 of 23) with videos related to

Sort By:
Pageof 3
Chemical Science|April 13, 2026
Black-box data: a new paradigm for biomedicine in the AI eraLuca Naef, Michael Bronstein
IEEE Transactions on Visualization and Computer Graphics|July 12, 2007
Calculus of nonrigid surfaces for geometry and texture manipulationAlexander Bronstein, Michael Bronstein, Ron Kimmel
Clinical Pharmacology and Therapeutics|January 3, 2024
The Future of Machine Learning Within Target Identification: Causality, Reversibility, and DruggabilityJake P Taylor-King, Michael Bronstein, David Roblin
Human Genomics|June 8, 2021
Predicting anticancer hyperfoods with graph convolutional networksGuadalupe Gonzalez, Shunwang Gong, Ivan Laponogov, et al.
Nature Biomedical Engineering|September 9, 2025
Combinatorial prediction of therapeutic perturbations using causally inspired neural networksGuadalupe Gonzalez, Xiang Lin, Isuru Herath, et al.
Biorxiv : the Preprint Server for Biology|January 23, 2024
Combinatorial prediction of therapeutic perturbations using causally-inspired neural networksGuadalupe Gonzalez, Xiang Lin, Isuru Herath, et al.
Cell Systems|November 16, 2023
A new age in protein design empowered by deep learningHamed Khakzad, Ilia Igashov, Arne Schneuing, et al.
Science (New York, N.Y.)|July 13, 2023
Using machine learning to decode animal communicationChristian Rutz, Michael Bronstein, Aza Raskin, et al.
Nature Communications|January 26, 2022
Interaction data are identifiable even across long periods of timeAna-Maria Creţu, Federico Monti, Stefano Marrone, et al.
Scientific Reports|July 5, 2019
HyperFoods: Machine intelligent mapping of cancer-beating molecules in foodsKirill Veselkov, Guadalupe Gonzalez, Shahad Aljifri, et al.
Pageof 3