Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1783407902157963264 |
---|---|
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. |
format | Online Article Text |
id | pubmed-6443823 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT tangbinhua recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology AT panzixiang recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology AT yinkang recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology AT khateebasif recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology |