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

Muhammad Adnan Pramudito

Showing results (1-10 of 12) with videos related to

Pageof 2
Sort By:
Bioengineering (Basel, Switzerland)|January 21, 2023
An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine LearningYunendah Nur Fuadah, Muhammad Adnan Pramudito, Ki Moo Lim
Toxicology Mechanisms and Methods|March 17, 2026
Interpretable Multi-Modality Consensus QSAR Framework: Integrating Machine and Deep Learning for Enhanced Multi-Endpoint Toxicity AssessmentFauzan Syarif Nursyafi, Muhammad Adnan Pramudito, Yunendah Nur Fuadah, et al.
Annals of Biomedical Engineering|January 26, 2026
Stacking Ensemble Machine Learning for Cardiac Safety Assessment Using hiPSC-CM MEA DataMuhammad Adnan Pramudito, Yunendah Nur Fuadah, Yoo Seok Kim, et al.
Frontiers in Physiology|October 20, 2023
Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variabilityYunendah Nur Fuadah, Ali Ikhsanul Qauli, Aroli Marcellinus, et al.
Scientific Reports|October 14, 2024
Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluationMuhammad Adnan Pramudito, Yunendah Nur Fuadah, Ali Ikhsanul Qauli, et al.
ACS Omega|January 1, 2025
QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End PointsYunendah Nur Fuadah, Muhammad Adnan Pramudito, Lulu Firdaus, et al.
CPT: Pharmacometrics & Systems Pharmacology|August 26, 2024
A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkersYunendah Nur Fuadah, Ali Ikhsanul Qauli, Muhammad Adnan Pramudito, et al.
Journal of Chemical Information and Modeling|April 13, 2026
ToxCML: A Hybrid mfCoQ-RASAR-Based Platform Integrating Consensus QSAR and Read-Across for Comprehensive Multi-End Point Toxicity AssessmentFauzan Syarif Nursyafi, Muhammad Adnan Pramudito, Yunendah Nur Fuadah, et al.
Annals of Biomedical Engineering|March 3, 2026
Improving In Silico Cardiac Safety Prediction by Consensus Averaging of Transmural Ventricular Cell ModelsNurul Qashri Mahardika T, Ali Ikhsanul Qauli, Yunendah Nur Fuadah, et al.
Archives of Toxicology|April 28, 2026
Integrating high-fidelity hiPSC-cardiomyocytes with AI-driven modeling for enhanced proarrhythmic risk assessmentSu-Bin Kim, Jaehun Lee, Jieun An, et al.
Pageof 2

Showing results (1-10 of 12) with videos related to

Sort By:
Pageof 2
Bioengineering (Basel, Switzerland)|January 21, 2023
An Optimal Approach for Heart Sound Classification Using Grid Search in Hyperparameter Optimization of Machine LearningYunendah Nur Fuadah, Muhammad Adnan Pramudito, Ki Moo Lim
Toxicology Mechanisms and Methods|March 17, 2026
Interpretable Multi-Modality Consensus QSAR Framework: Integrating Machine and Deep Learning for Enhanced Multi-Endpoint Toxicity AssessmentFauzan Syarif Nursyafi, Muhammad Adnan Pramudito, Yunendah Nur Fuadah, et al.
Annals of Biomedical Engineering|January 26, 2026
Stacking Ensemble Machine Learning for Cardiac Safety Assessment Using hiPSC-CM MEA DataMuhammad Adnan Pramudito, Yunendah Nur Fuadah, Yoo Seok Kim, et al.
Frontiers in Physiology|October 20, 2023
Machine learning approach to evaluate TdP risk of drugs using cardiac electrophysiological model including inter-individual variabilityYunendah Nur Fuadah, Ali Ikhsanul Qauli, Aroli Marcellinus, et al.
Scientific Reports|October 14, 2024
Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluationMuhammad Adnan Pramudito, Yunendah Nur Fuadah, Ali Ikhsanul Qauli, et al.
ACS Omega|January 1, 2025
QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End PointsYunendah Nur Fuadah, Muhammad Adnan Pramudito, Lulu Firdaus, et al.
CPT: Pharmacometrics & Systems Pharmacology|August 26, 2024
A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkersYunendah Nur Fuadah, Ali Ikhsanul Qauli, Muhammad Adnan Pramudito, et al.
Journal of Chemical Information and Modeling|April 13, 2026
ToxCML: A Hybrid mfCoQ-RASAR-Based Platform Integrating Consensus QSAR and Read-Across for Comprehensive Multi-End Point Toxicity AssessmentFauzan Syarif Nursyafi, Muhammad Adnan Pramudito, Yunendah Nur Fuadah, et al.
Annals of Biomedical Engineering|March 3, 2026
Improving In Silico Cardiac Safety Prediction by Consensus Averaging of Transmural Ventricular Cell ModelsNurul Qashri Mahardika T, Ali Ikhsanul Qauli, Yunendah Nur Fuadah, et al.
Archives of Toxicology|April 28, 2026
Integrating high-fidelity hiPSC-cardiomyocytes with AI-driven modeling for enhanced proarrhythmic risk assessmentSu-Bin Kim, Jaehun Lee, Jieun An, et al.
Pageof 2