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离子通道生物信息学中的人工智能、机器学习与深度学习

Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics.

作者信息

Ashrafuzzaman Md

机构信息

Department of Biochemistry, College of Science, King Saud University, Riyadh 11451, Saudi Arabia.

出版信息

Membranes (Basel). 2021 Aug 31;11(9):672. doi: 10.3390/membranes11090672.

Abstract

Ion channels are linked to important cellular processes. For more than half a century, we have been learning various structural and functional aspects of ion channels using biological, physiological, biochemical, and biophysical principles and techniques. In recent days, bioinformaticians and biophysicists having the necessary expertise and interests in computer science techniques including versatile algorithms have started covering a multitude of physiological aspects including especially evolution, mutations, and genomics of functional channels and channel subunits. In these focused research areas, the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms and associated models have been found very popular. With the help of available articles and information, this review provide an introduction to this novel research trend. Ion channel understanding is usually made considering the structural and functional perspectives, gating mechanisms, transport properties, channel protein mutations, etc. Focused research on ion channels and related findings over many decades accumulated huge data which may be utilized in a specialized scientific manner to fast conclude pinpointed aspects of channels. AI, ML, and DL techniques and models may appear as helping tools. This review aims at explaining the ways we may use the bioinformatics techniques and thus draw a few lines across the avenue to let the ion channel features appear clearer.

摘要

离子通道与重要的细胞过程相关联。半个多世纪以来,我们一直在运用生物学、生理学、生物化学和生物物理原理及技术来了解离子通道的各种结构和功能方面。近年来,对包括通用算法在内的计算机科学技术有必要专业知识和兴趣的生物信息学家和生物物理学家,已开始涉足众多生理学领域,尤其是功能通道和通道亚基的进化、突变及基因组学。在这些重点研究领域,人工智能(AI)、机器学习(ML)和深度学习(DL)算法及相关模型的应用已十分普遍。借助现有文章和信息,本综述对这一新兴研究趋势进行介绍。对离子通道的理解通常从结构和功能角度、门控机制、转运特性、通道蛋白突变等方面进行。数十年来对离子通道的重点研究及相关发现积累了海量数据,这些数据可通过专门的科学方式加以利用,以便快速得出关于通道的精准方面。AI、ML和DL技术及模型可能会成为辅助工具。本综述旨在解释我们可以使用生物信息学技术的方式,从而在这条道路上划几条线,使离子通道的特征更加清晰。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ce7/8467682/99c9be88e6e5/membranes-11-00672-g001.jpg

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