Explainable artificial intelligence (XAI) to find optimal in-silico biomarkers for cardiac drug toxicity evaluation
Abstract The Comprehensive In-vitro Proarrhythmia Assay (CiPA) initiative aims to refine the assessment of drug-induced torsades de pointes (TdP) risk, utilizing computational models to predict cardiac drug toxicity. Despite advancements in machine learning applications for this purpose, the specifi...
Saved in:
| Main Authors: | Muhammad Adnan Pramudito, Yunendah Nur Fuadah, Ali Ikhsanul Qauli, Aroli Marcellinus, Ki Moo Lim |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2024-10-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-71169-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A stacking ensemble machine learning model for evaluating cardiac toxicity of drugs based on in silico biomarkers
by: Yunendah Nur Fuadah, et al.
Published: (2024-12-01) -
QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points
by: Yunendah Nur Fuadah, et al.
Published: (2024-12-01) -
A novel XAI framework for explainable AI-ECG using generative counterfactual XAI (GCX)
by: Jong-Hwan Jang, et al.
Published: (2025-07-01) -
An Overview of the Empirical Evaluation of Explainable AI (XAI): A Comprehensive Guideline for User-Centered Evaluation in XAI
by: Sidra Naveed, et al.
Published: (2024-12-01) -
Do explainable AI (XAI) methods improve the acceptance of AI in clinical practice? An evaluation of XAI methods on Gleason grading
by: Robin Manz, et al.
Published: (2025-03-01)