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Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics
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 biophy...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2021
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467682/ https://www.ncbi.nlm.nih.gov/pubmed/34564489 http://dx.doi.org/10.3390/membranes11090672 |
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author | Ashrafuzzaman, Md. |
author_facet | Ashrafuzzaman, Md. |
author_sort | Ashrafuzzaman, Md. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8467682 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-84676822021-09-27 Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics Ashrafuzzaman, Md. Membranes (Basel) Review 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. MDPI 2021-08-31 /pmc/articles/PMC8467682/ /pubmed/34564489 http://dx.doi.org/10.3390/membranes11090672 Text en © 2021 by the author. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Review Ashrafuzzaman, Md. Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics |
title | Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics |
title_full | Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics |
title_fullStr | Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics |
title_full_unstemmed | Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics |
title_short | Artificial Intelligence, Machine Learning and Deep Learning in Ion Channel Bioinformatics |
title_sort | artificial intelligence, machine learning and deep learning in ion channel bioinformatics |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8467682/ https://www.ncbi.nlm.nih.gov/pubmed/34564489 http://dx.doi.org/10.3390/membranes11090672 |
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