Michael J. Keiser

According to our database, Michael J. Keiser authored at least 15 papers between 2008 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Autoregressive fragment-based diffusion for pocket-aware ligand design.

Mahdi Ghorbani, Leo Gendelev, Paul Beroza, Michael J. Keiser

CoRR, 2024

2023

The scalable precision medicine open knowledge engine (SPOKE): a massive knowledge graph of biomedical information.

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

Bioinform., February, 2023

2022

Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics.

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

Nat. Mach. Intell., 2022

Prioritizing Virtual Screening with Interpretable Interaction Fingerprints.

Alexandre V. Fassio, Laura Shub, Luca Ponzoni, Jessica McKinley, Matthew J. O'Meara, Rafaela Salgado Ferreira, Michael J. Keiser, Raquel Cardoso de Melo Minardi

J. Chem. Inf. Model., 2022

Learning Melanocytic Cell Masks from Adjacent Stained Tissue.

Mikio Tada, Maria L. Wei, Michael J. Keiser

CoRR, 2022

A single-cell gene expression language model.

William Connell, Umair Khan, Michael J. Keiser

CoRR, 2022

A Biomedical Open Knowledge Network Harnesses the Power of AI to Understand Deep Human Biology.

Sergio E. Baranzini, Katy Börner, John H. 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

AI Mag., 2022

2021

Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models.

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

npj Digit. Medicine, 2021

Robust Semantic Interpretability: Revisiting Concept Activation Vectors.

Jacob Pfau, Albert T. Young, Jerome Wei, Maria L. Wei, Michael J. Keiser

CoRR, 2021

2020

Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity Prediction.

Elena L. Cáceres, Nicholas C. Mew, Michael J. Keiser

J. Chem. Inf. Model., 2020

Attention-Based Learning on Molecular Ensembles.

Kangway V. Chuang, Michael J. Keiser

CoRR, 2020

2019

Global Saliency: Aggregating Saliency Maps to Assess Dataset Artefact Bias.

Jacob Pfau, Albert T. Young, Maria L. Wei, Michael J. Keiser

CoRR, 2019

2018

Predicted Biological Activity of Purchasable Chemical Space.

John J. Irwin, Garrett Gaskins, Teague Sterling, Michael M. Mysinger, Michael J. Keiser

J. Chem. Inf. Model., 2018

2009

A Mapping of Drug Space from the Viewpoint of Small Molecule Metabolism.

James Corey Adams, Michael J. Keiser, Li Basuino, Henry F. Chambers, Deok-Sun Lee, Olaf G. Wiest, Patricia C. Babbitt

PLoS Comput. Biol., 2009

2008

Quantifying the Relationships among Drug Classes.

Jérôme Hert, Michael J. Keiser, John J. Irwin, Tudor I. Oprea, Brian K. Shoichet

J. Chem. Inf. Model., 2008