Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation

Abstract Many studies have revealed changes in specific protein channels due to physiological causes such as mutation and their effects on action potential duration changes. However, no studies have been conducted to predict the type of protein channel abnormalities that occur through an action pote...

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Autores principales:	Da Un Jeong, Ki Moo Lim
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4673dd372b6c45429ae99addc5bc2c75
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spelling	oai:doaj.org-article:4673dd372b6c45429ae99addc5bc2c752021-12-02T14:37:39ZArtificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation10.1038/s41598-021-87578-02045-2322https://doaj.org/article/4673dd372b6c45429ae99addc5bc2c752021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87578-0https://doaj.org/toc/2045-2322Abstract Many studies have revealed changes in specific protein channels due to physiological causes such as mutation and their effects on action potential duration changes. However, no studies have been conducted to predict the type of protein channel abnormalities that occur through an action potential (AP) shape. Therefore, in this study, we aim to predict the ion channel conductance that is altered from various AP shapes using a machine learning algorithm. We perform electrophysiological simulations using a single-cell model to obtain AP shapes based on variations in the ion channel conductance. In the AP simulation, we increase and decrease the conductance of each ion channel at a constant rate, resulting in 1,980 AP shapes and one standard AP shape without any changes in the ion channel conductance. Subsequently, we calculate the AP difference shapes between them and use them as the input of the machine learning model to predict the changed ion channel conductance. In this study, we demonstrate that the changed ion channel conductance can be predicted with high prediction accuracy, as reflected by an F1 score of 0.985, using only AP shapes and simple machine learning.Da Un JeongKi Moo LimNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Da Un Jeong Ki Moo Lim Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation
description	Abstract Many studies have revealed changes in specific protein channels due to physiological causes such as mutation and their effects on action potential duration changes. However, no studies have been conducted to predict the type of protein channel abnormalities that occur through an action potential (AP) shape. Therefore, in this study, we aim to predict the ion channel conductance that is altered from various AP shapes using a machine learning algorithm. We perform electrophysiological simulations using a single-cell model to obtain AP shapes based on variations in the ion channel conductance. In the AP simulation, we increase and decrease the conductance of each ion channel at a constant rate, resulting in 1,980 AP shapes and one standard AP shape without any changes in the ion channel conductance. Subsequently, we calculate the AP difference shapes between them and use them as the input of the machine learning model to predict the changed ion channel conductance. In this study, we demonstrate that the changed ion channel conductance can be predicted with high prediction accuracy, as reflected by an F1 score of 0.985, using only AP shapes and simple machine learning.
format	article
author	Da Un Jeong Ki Moo Lim
author_facet	Da Un Jeong Ki Moo Lim
author_sort	Da Un Jeong
title	Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation
title_short	Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation
title_full	Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation
title_fullStr	Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation
title_full_unstemmed	Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation
title_sort	artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4673dd372b6c45429ae99addc5bc2c75
work_keys_str_mv	AT daunjeong artificialneuralnetworkmodelforpredictingchangesinionchannelconductancebasedoncardiacactionpotentialshapesgeneratedviasimulation AT kimoolim artificialneuralnetworkmodelforpredictingchangesinionchannelconductancebasedoncardiacactionpotentialshapesgeneratedviasimulation
_version_	1718391002780139520

Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation

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