Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms.
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A new paradigm for predicting risk of Torsades de Pointes during drug development: Commentary on: "Improved prediction of drug-induced Torsades de Pointes through simulations of dynamics and machine learning algorithms".Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk AssessmentHuman In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic CardiotoxicityMechanistic Systems Modeling to Improve Understanding and Prediction of Cardiotoxicity Caused by Targeted Cancer Therapeutics.Revealing kinetics and state-dependent binding properties of IKur-targeting drugs that maximize atrial fibrillation selectivity.Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features.In Silico Assessment of Efficacy and Safety of IKur Inhibitors in Chronic Atrial Fibrillation: Role of Kinetics and State-Dependence of Drug Binding.In Vitro and In Silico Risk Assessment in Acquired Long QT Syndrome: The Devil Is in the Details.Global Optimization of Ventricular Myocyte Model to Multi-Variable Objective Improves Predictions of Drug-Induced Torsades de Pointes.Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block.Unlocking data sets by calibrating populations of models to data density: A study in atrial electrophysiology.Complex versus simple models: ion-channel cardiac toxicity prediction.Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator† †Electronic supplementary information (ESI) available. See DOI: 10.1039/C7TX00141J.Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator.How to Connect Cardiac Excitation to the Atomic Interactions of Ion Channels.Response to "Complexity vs. Simplicity: The Winner Is?" Mechanism-Based Classifiers Provide More Than Just Classification.Complexity vs. Simplicity: The Winner Is?Quantitative approach for cardiac risk assessment and interpretation in tuberculosis drug development.Editorial: Safety Pharmacology - Risk Assessment QT Interval Prolongation and Beyond.A Heart for Diversity: Simulating Variability in Cardiac Arrhythmia Research
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P2860
Improved Prediction of Drug-Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms.
description
2016 nî lūn-bûn
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2016年の論文
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2016年学术文章
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2016年学术文章
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2016年学术文章
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2016年学术文章
@zh-hans
2016年学术文章
@zh-my
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2016年學術文章
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2016年學術文章
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name
Improved Prediction of Drug-In ...... d Machine Learning Algorithms.
@en
Improved Prediction of Drug-In ...... d Machine Learning Algorithms.
@nl
type
label
Improved Prediction of Drug-In ...... d Machine Learning Algorithms.
@en
Improved Prediction of Drug-In ...... d Machine Learning Algorithms.
@nl
prefLabel
Improved Prediction of Drug-In ...... d Machine Learning Algorithms.
@en
Improved Prediction of Drug-In ...... d Machine Learning Algorithms.
@nl
P2860
P356
P1476
Improved Prediction of Drug-In ...... d Machine Learning Algorithms.
@en
P2093
M Cummins Lancaster
P2860
P304
P356
10.1002/CPT.367
P407
P577
2016-05-20T00:00:00Z