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For: Longobardi S, Lewalle A, Coveney S, Sjaastad I, Espe EKS, Louch WE, Musante CJ, Sher A, Niederer SA. Predicting left ventricular contractile function via Gaussian process emulation in aortic-banded rats. Philos Trans A Math Phys Eng Sci 2020;378:20190334. [PMID: 32448071 DOI: 10.1098/rsta.2019.0334] [Cited by in Crossref: 17] [Cited by in F6Publishing: 18] [Article Influence: 5.7] [Reference Citation Analysis]
Number Citing Articles
1 Donnelly J, Abolfathi S, Pearson J, Chatrabgoun O, Daneshkhah A. Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model. Water Res 2022;225:119100. [PMID: 36155010 DOI: 10.1016/j.watres.2022.119100] [Reference Citation Analysis]
2 Regazzoni F, Salvador M, Dede’ L, Quarteroni A. A machine learning method for real-time numerical simulations of cardiac electromechanics. Computer Methods in Applied Mechanics and Engineering 2022;393:114825. [DOI: 10.1016/j.cma.2022.114825] [Cited by in Crossref: 3] [Cited by in F6Publishing: 4] [Article Influence: 3.0] [Reference Citation Analysis]
3 Jung A, Gsell MAF, Augustin CM, Plank G. An Integrated Workflow for Building Digital Twins of Cardiac Electromechanics—A Multi-Fidelity Approach for Personalising Active Mechanics. Mathematics 2022;10:823. [DOI: 10.3390/math10050823] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]
4 Lewalle A, Campbell KS, Campbell SG, Milburn GN, Niederer SA. Functional and structural differences between skinned and intact muscle preparations. J Gen Physiol 2022;154:e202112990. [PMID: 35045156 DOI: 10.1085/jgp.202112990] [Reference Citation Analysis]
5 Cicci L, Fresca S, Pagani S, Manzoni A, Quarteroni A. . MINE 2022;5:1-38. [DOI: 10.3934/mine.2023026] [Cited by in F6Publishing: 2] [Reference Citation Analysis]
6 Longobardi S, Sher A, Niederer SA. In silico identification of potential calcium dynamics and sarcomere targets for recovering left ventricular function in rat heart failure with preserved ejection fraction. PLoS Comput Biol 2021;17:e1009646. [PMID: 34871310 DOI: 10.1371/journal.pcbi.1009646] [Cited by in Crossref: 2] [Cited by in F6Publishing: 3] [Article Influence: 1.0] [Reference Citation Analysis]
7 Zaman MS, Dhamala J, Bajracharya P, Sapp JL, Horácek BM, Wu KC, Trayanova NA, Wang L. Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning. Front Physiol 2021;12:740306. [PMID: 34759835 DOI: 10.3389/fphys.2021.740306] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 1.5] [Reference Citation Analysis]
8 Maso Talou GD, Babarenda Gamage TP, Nash MP. Efficient Ventricular Parameter Estimation Using AI-Surrogate Models. Front Physiol 2021;12:732351. [PMID: 34721062 DOI: 10.3389/fphys.2021.732351] [Reference Citation Analysis]
9 Anantharaman R, Abdelrehim A, Jain A, Pal A, Sharp D, Utkarsh, Rackauckas C. Stably Accelerating Stiff Quantitative Systems Pharmacology Models: Continuous-Time Echo State Networks as Implicit Machine Learning.. [DOI: 10.1101/2021.10.10.463808] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.5] [Reference Citation Analysis]
10 Coveney S, Corrado C, Oakley JE, Wilkinson RD, Niederer SA, Clayton RH. Bayesian Calibration of Electrophysiology Models Using Restitution Curve Emulators. Front Physiol 2021;12:693015. [PMID: 34366883 DOI: 10.3389/fphys.2021.693015] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
11 Pagani S, Manzoni A. Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning. Int J Numer Method Biomed Eng 2021;37:e3450. [PMID: 33599106 DOI: 10.1002/cnm.3450] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 3.5] [Reference Citation Analysis]
12 Rodero C, Strocchi M, Marciniak M, Longobardi S, Whitaker J, O'Neill MD, Gillette K, Augustin C, Plank G, Vigmond EJ, Lamata P, Niederer SA. Linking statistical shape models and simulated function in the healthy adult human heart. PLoS Comput Biol 2021;17:e1008851. [PMID: 33857152 DOI: 10.1371/journal.pcbi.1008851] [Cited by in Crossref: 19] [Cited by in F6Publishing: 22] [Article Influence: 9.5] [Reference Citation Analysis]
13 Cai L, Ren L, Wang Y, Xie W, Zhu G, Gao H. Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium. R Soc Open Sci 2021;8:201121. [PMID: 33614068 DOI: 10.1098/rsos.201121] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 7.5] [Reference Citation Analysis]
14 Longobardi S, Sher A, Niederer SA. In Silico Mapping of the Omecamtiv Mecarbil Effects from the Sarcomere to the Whole-Heart and Back Again. Functional Imaging and Modeling of the Heart 2021. [DOI: 10.1007/978-3-030-78710-3_39] [Cited by in Crossref: 1] [Article Influence: 0.5] [Reference Citation Analysis]
15 Fresca S, Manzoni A, Dedè L, Quarteroni A. Deep learning-based reduced order models in cardiac electrophysiology. PLoS One 2020;15:e0239416. [PMID: 33002014 DOI: 10.1371/journal.pone.0239416] [Cited by in Crossref: 22] [Cited by in F6Publishing: 23] [Article Influence: 7.3] [Reference Citation Analysis]
16 Mirams GR, Niederer SA, Clayton RH. The fickle heart: uncertainty quantification in cardiac and cardiovascular modelling and simulation. Philos Trans A Math Phys Eng Sci 2020;378:20200119. [PMID: 32448073 DOI: 10.1098/rsta.2020.0119] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 2.7] [Reference Citation Analysis]