Michael J. Keiser

Publication Activity (10 Years)

Years Active: 2008-2024
Publications (10 Years): 14

Top Topics

Virtual Screening

Proximity Graph

Knowledge Acquisition

Top Venues

J. Chem. Inf. Model.

Nat. Mach. Intell.

Publications

Mahdi Ghorbani, Leo Gendelev, Paul Beroza, Michael J. Keiser
Autoregressive fragment-based diffusion for pocket-aware ligand design. CoRR (2024)
Zachary J. Gale-Day, Laura Shub, Kangway V. Chuang, Michael J. Keiser
Proximity Graph Networks: Predicting Ligand Affinity with Message Passing Neural Networks. J. Chem. Inf. Model. 64 (14) (2024)
John H. Morris, Karthik Soman, Rabia E. Akbas, Xiaoyuan Zhou, Brett Smith, Elaine C. Meng, Conrad C. Huang, Gabriel Cerono, Gundolf Schenk, Angela Rizk-Jackson, Adil Harroud, Lauren M. Sanders, Sylvain V. Costes, Krish Bharat, Arjun Chakraborty, Alexander R. Pico, Taline Mardirossian, Michael J. Keiser, Alice Tang, Josef Hardi, Yongmei Shi, Mark A. Musen, Sharat Israni, Sui Huang, Peter W. Rose, Charlotte A. Nelson, Sergio E. Baranzini
The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information. Bioinform. 39 (2) (2023)
Alexandre V. Fassio, Laura Shub, Luca Ponzoni, Jessica McKinley, Matthew J. O'meara, Rafaela Salgado Ferreira, Michael J. Keiser, Raquel Cardoso de Melo Minardi
Prioritizing Virtual Screening with Interpretable Interaction Fingerprints. J. Chem. Inf. Model. 62 (18) (2022)
Sergio E. Baranzini, Katy Börner, John Morris, Charlotte A. Nelson, Karthik Soman, Erica Schleimer, Michael J. Keiser, Mark A. Musen, Roger Pearce, Tahsin Reza, Brett Smith, Bruce William Herr II, Boris Oskotsky, Angela Rizk-Jackson, Katherine P. Rankin, Stephan J. Sanders, Riley Bove, Peter W. Rose, Sharat Israni, Sui Huang
A Biomedical Open Knowledge Network Harnesses the Power of AI to Understand Deep Human Biology. AI Mag. 43 (1) (2022)
Mikio Tada, Maria L. Wei, Michael J. Keiser
Learning Melanocytic Cell Masks from Adjacent Stained Tissue. CoRR (2022)
Daniel R. Wong, Jay Conrad, Noah R. Johnson, Jacob Ayers, Annelies Laeremans, Joanne C. Lee, Jisoo Lee, Stanley B. Prusiner, Sourav Bandyopadhyay, Atul J. Butte, Nick A. Paras, Michael J. Keiser
Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics. Nat. Mach. Intell. 4 (6) (2022)
William Connell, Umair Khan, Michael J. Keiser
A single-cell gene expression language model. CoRR (2022)
Albert T. Young, Kristen Fernandez, Jacob Pfau, Rasika Reddy, Nhat Anh Cao, Max Y. von Franque, Arjun Johal, Benjamin V. Wu, Rachel R. Wu, Jennifer Y. Chen, Raj P. Fadadu, Juan A. Vasquez, Andrew Tam, Michael J. Keiser, Maria L. Wei
Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models. npj Digit. Medicine 4 (2021)
Jacob Pfau, Albert T. Young, Jerome Wei, Maria L. Wei, Michael J. Keiser
Robust Semantic Interpretability: Revisiting Concept Activation Vectors. CoRR (2021)
Elena L. Cáceres, Nicholas C. Mew, Michael J. Keiser
Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction. J. Chem. Inf. Model. 60 (12) (2020)
Kangway V. Chuang, Michael J. Keiser
Attention-Based Learning on Molecular Ensembles. CoRR (2020)
Jacob Pfau, Albert T. Young, Maria L. Wei, Michael J. Keiser
Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias. CoRR (2019)
John J. Irwin, Garrett Gaskins, Teague Sterling, Michael M. Mysinger, Michael J. Keiser
Predicted Biological Activity of Purchasable Chemical Space. J. Chem. Inf. Model. 58 (1) (2018)
James Corey Adams, Michael J. Keiser, Li Basuino, Henry F. Chambers, Deok-Sun Lee, Olaf G. Wiest, Patricia C. Babbitt
A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism. PLoS Comput. Biol. 5 (8) (2009)
Jérôme Hert, Michael J. Keiser, John J. Irwin, Tudor I. Oprea, Brian K. Shoichet
Quantifying the Relationships among Drug Classes. J. Chem. Inf. Model. 48 (4) (2008)