ArticleScientific reports2021
Artificial neural network model for predicting changes in ion channel conductance based on cardiac action potential shapes generated via simulation.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review.Journal of personalized medicine · 2025Review
- Machine learning in optimization of nonwoven fabric bending rigidity in spunlace production line.Scientific reports · 2023Article
- A pilot study of ion current estimation by ANN from action potential waveforms.Journal of biological physics · 2022Article
- To what extent naringenin binding and membrane depolarization shape mitoBK channel gating-A machine learning approach.PLoS computational biology · 2022Article
- Proarrhythmic risk assessment of drugs by dVCPT: pharmacometrics & systems pharmacology · 2022Article
- The new SUMPOT to predict postoperative complications using an Artificial Neural Network.Scientific reports · 2021Observational
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Authors and funding
2 authors.
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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.
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