Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Michael J Keiser

Showing results (1-10 of 55) with videos related to

Pageof 6
Sort By:
Methods in Molecular Biology (Clifton, N.J.)|September 4, 2009
Off-target networks derived from ligand set similarityMichael J Keiser, Jérôme Hert
ACS Chemical Biology|October 20, 2018
Adversarial Controls for Scientific Machine LearningKangway V Chuang, Michael J Keiser
Science (New York, N.Y.)|November 17, 2018
Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"Kangway V Chuang, Michael J Keiser
Journal of Chemical Information and Modeling|October 3, 2024
Retrieval Augmented Docking Using Hierarchical Navigable Small WorldsBrendan W Hall, Michael J Keiser
Journal of Medicinal Chemistry|May 6, 2020
Learning Molecular Representations for Medicinal ChemistryKangway V Chuang, Laura M Gunsalus, Michael J Keiser
Communications Biology|September 15, 2024
Learning chemical sensitivity reveals mechanisms of cellular responseWilliam Connell, Kristle Garcia, Hani Goodarzi, et al.
Biorxiv : the Preprint Server for Biology|December 4, 2023
ChromaFactor: deconvolution of single-molecule chromatin organization with non-negative matrix factorizationLaura M Gunsalus, Michael J Keiser, Katherine S Pollard
Cell Genomics|October 23, 2023
<i>In silico</i> discovery of repetitive elements as key sequence determinants of 3D genome foldingLaura M Gunsalus, Michael J Keiser, Katherine S Pollard
Journal of Chemical Information and Modeling|November 27, 2020
Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity PredictionElena L Cáceres, Nicholas C Mew, Michael J Keiser
Biochemistry|November 10, 2010
The chemical basis of pharmacologyMichael J Keiser, John J Irwin, Brian K Shoichet
Pageof 6

Showing results (1-10 of 55) with videos related to

Sort By:
Pageof 6
Methods in Molecular Biology (Clifton, N.J.)|September 4, 2009
Off-target networks derived from ligand set similarityMichael J Keiser, Jérôme Hert
ACS Chemical Biology|October 20, 2018
Adversarial Controls for Scientific Machine LearningKangway V Chuang, Michael J Keiser
Science (New York, N.Y.)|November 17, 2018
Comment on "Predicting reaction performance in C-N cross-coupling using machine learning"Kangway V Chuang, Michael J Keiser
Journal of Chemical Information and Modeling|October 3, 2024
Retrieval Augmented Docking Using Hierarchical Navigable Small WorldsBrendan W Hall, Michael J Keiser
Journal of Medicinal Chemistry|May 6, 2020
Learning Molecular Representations for Medicinal ChemistryKangway V Chuang, Laura M Gunsalus, Michael J Keiser
Communications Biology|September 15, 2024
Learning chemical sensitivity reveals mechanisms of cellular responseWilliam Connell, Kristle Garcia, Hani Goodarzi, et al.
Biorxiv : the Preprint Server for Biology|December 4, 2023
ChromaFactor: deconvolution of single-molecule chromatin organization with non-negative matrix factorizationLaura M Gunsalus, Michael J Keiser, Katherine S Pollard
Cell Genomics|October 23, 2023
<i>In silico</i> discovery of repetitive elements as key sequence determinants of 3D genome foldingLaura M Gunsalus, Michael J Keiser, Katherine S Pollard
Journal of Chemical Information and Modeling|November 27, 2020
Adding Stochastic Negative Examples into Machine Learning Improves Molecular Bioactivity PredictionElena L Cáceres, Nicholas C Mew, Michael J Keiser
Biochemistry|November 10, 2010
The chemical basis of pharmacologyMichael J Keiser, John J Irwin, Brian K Shoichet
Pageof 6