基于深度学习的自动膜片钳记录离子通道动力学分析（add . Sci. 12/2025）

IF 14.1 1区材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Advanced Science Pub Date : 2025-03-27 DOI:10.1002/advs.202570080

Shengjie Yang, Jiaqi Xue, Ziqi Li, Shiqing Zhang, Zhang Zhang, Zhifeng Huang, Ken Kin Lam Yung, King Wai Chiu Lai

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引用次数: 0

摘要

这项工作展示了人工智能在分析离子通道记录中的集成。它强调了人工智能驱动的离子通道动力学评估和细胞膜上离子通道活性的可视化。该框架自动处理膜片钳数据，提供精确的离子通道动力学，为推进电生理学研究提供了强大的工具。详情请参阅景伟超莱及其同事的文章2404166。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording (Adv. Sci. 12/2025)

查看原文本刊更多论文

Deep Learning-Based Ion Channel Kinetics Analysis for Automated Patch Clamp Recording (Adv. Sci. 12/2025)

Deep Learning

This work showcases the integration of artificial intelligence in analyzing ion channel recordings. It highlights the AI-driven evaluation of ion channel kinetics and the visualization of ion channel activity on the cell membrane. The framework automatically processes patch clamp data, delivering precise ion channel kinetics, offering a powerful tool for advancing electrophysiological research. More details can be found in article number 2404166 by King Wai Chiu Lai and co-workers.

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来源期刊

Advanced Science CHEMISTRY, MULTIDISCIPLINARYNANOSCIENCE &-NANOSCIENCE & NANOTECHNOLOGY

CiteScore

18.90

自引率

2.60%

发文量

1602

审稿时长

1.9 months

期刊介绍： Advanced Science is a prestigious open access journal that focuses on interdisciplinary research in materials science, physics, chemistry, medical and life sciences, and engineering. The journal aims to promote cutting-edge research by employing a rigorous and impartial review process. It is committed to presenting research articles with the highest quality production standards, ensuring maximum accessibility of top scientific findings. With its vibrant and innovative publication platform, Advanced Science seeks to revolutionize the dissemination and organization of scientific knowledge.