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.
Highlights
IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
This review aims at explaining the ways we may use the bioinformatics techniques and draw a few lines across the avenue to let the ion channel features appear clearer
Ancestral nodes, where the nervous systems probably originated, were found to experience not-so-large expansions. This suggests for the origin of nerves not to experience any immediate complexity bursts, instead, the complexity of evolution perhaps experienced a rather slow fuse in the stem animals, which got followed by gene gains and losses independently
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. A review has described approaches that are based on simultaneous use of the systems biology and the ML in order to access the gene and the protein druggability [6] It elaborated on the sources of data, algorithms, and performance of different methods. It is clear that artificial techniques, models, and algorithms are utilized to program various ion channel features, including classification of channels, channel subunit proteins, or even amino acids and genes, which addresses evolution, modern engineering, and various other related aspects. AI, ML, and DL have a lot of involvement in this new area Experimental address and their theoretical analysis have produced so much data that we need these artificial techniques to grasp most about ion channels’ various features in a simplistic manner, using models and algorithms that are made possible using the power of AI, including its subfields ML and DL
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