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Recent Advances of Deep Learning in Bioinformatics and Computational Biology

Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. Deep learning, as an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry. We highlig...

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Detalles Bibliográficos
Autores principales: Tang, Binhua, Pan, Zixiang, Yin, Kang, Khateeb, Asif
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443823/
https://www.ncbi.nlm.nih.gov/pubmed/30972100
http://dx.doi.org/10.3389/fgene.2019.00214
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author Tang, Binhua
Pan, Zixiang
Yin, Kang
Khateeb, Asif
author_facet Tang, Binhua
Pan, Zixiang
Yin, Kang
Khateeb, Asif
author_sort Tang, Binhua
collection PubMed
description Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. Deep learning, as an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry. We highlight the difference and similarity in widely utilized models in deep learning studies, through discussing their basic structures, and reviewing diverse applications and disadvantages. We anticipate the work can serve as a meaningful perspective for further development of its theory, algorithm and application in bioinformatic and computational biology.
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spelling pubmed-64438232019-04-10 Recent Advances of Deep Learning in Bioinformatics and Computational Biology Tang, Binhua Pan, Zixiang Yin, Kang Khateeb, Asif Front Genet Genetics Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. Deep learning, as an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry. We highlight the difference and similarity in widely utilized models in deep learning studies, through discussing their basic structures, and reviewing diverse applications and disadvantages. We anticipate the work can serve as a meaningful perspective for further development of its theory, algorithm and application in bioinformatic and computational biology. Frontiers Media S.A. 2019-03-26 /pmc/articles/PMC6443823/ /pubmed/30972100 http://dx.doi.org/10.3389/fgene.2019.00214 Text en Copyright © 2019 Tang, Pan, Yin and Khateeb. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Genetics
Tang, Binhua
Pan, Zixiang
Yin, Kang
Khateeb, Asif
Recent Advances of Deep Learning in Bioinformatics and Computational Biology
title Recent Advances of Deep Learning in Bioinformatics and Computational Biology
title_full Recent Advances of Deep Learning in Bioinformatics and Computational Biology
title_fullStr Recent Advances of Deep Learning in Bioinformatics and Computational Biology
title_full_unstemmed Recent Advances of Deep Learning in Bioinformatics and Computational Biology
title_short Recent Advances of Deep Learning in Bioinformatics and Computational Biology
title_sort recent advances of deep learning in bioinformatics and computational biology
topic Genetics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6443823/
https://www.ncbi.nlm.nih.gov/pubmed/30972100
http://dx.doi.org/10.3389/fgene.2019.00214
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