Machine learning in drug development: characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification

DOI10.1016/j.cma.2019.01.033zbMath1440.62371OpenAlexW2912518841WikidataQ99629392 ScholiaQ99629392MaRDI QIDQ1987901

Kristen Matsuno, Jiang Yao, Paris Perdikaris, Francisco Sahli Costabal, Ellen Kuhl

Publication date: 16 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cma.2019.01.033

zbMATH Keywords

sensitivity analysisfinite element analysismachine learninguncertainty quantificationGaussian process regressiondrug development

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (8)

How drugs modulate the performance of the human heart ⋮ Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression ⋮ Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models ⋮ Modelling and simulation for preclinical cardiac safety assessment of drugs with human iPSC-derived cardiomyocytes ⋮ The reproduction number of COVID-19 and its correlation with public health interventions ⋮ Visualizing the invisible: the effect of asymptomatic transmission on the outbreak dynamics of COVID-19 ⋮ Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models ⋮ 8 Reduced-order modeling for applications to the cardiovascular system

Uses Software

• L-BFGS
• ABAQUS

Cites Work

• Unnamed Item
• Atrial and ventricular fibrillation: computational simulation of spiral waves in cardiac tissue
• A fully implicit finite element method for bidomain models of cardiac electromechanics
• Computational modeling of electrochemical coupling: a novel finite element approach towards ionic models for cardiac electrophysiology
• On the limited memory BFGS method for large scale optimization
• A space-fractional monodomain model for cardiac electrophysiology combining anisotropy and heterogeneity on realistic geometries
• The living heart project: a robust and integrative simulator for human heart function
• The importance of mechano-electrical feedback and inertia in cardiac electromechanics
• A generalized multi-resolution expansion for uncertainty propagation with application to cardiovascular modeling
• Integrated heart -- coupling multiscale and multiphysics models for the simulation of the cardiac function
• Computational modeling of non-linear diffusion in cardiac electrophysiology: a novel porous-medium approach
• Electromechanics of the heart: a unified approach to the strongly coupled excitation-contraction problem
• A Bayesian Approach for Global Sensitivity Analysis of (Multifidelity) Computer Codes
• Computational modeling of cardiac electrophysiology: A novel finite element approach
• Mathematical models of the electrical action potential of Purkinje fibre cells
• Computational modeling of electrocardiograms: A finite element approach toward cardiac excitation
• Probabilistic Sensitivity Analysis of Complex Models: A Bayesian Approach
• Predicting the output from a complex computer code when fast approximations are available
• Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets

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