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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Frontiers Media S.A.
2019-03-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fgene.2019.00214/full |
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doaj-6c8a98be290f43d7a9568a58ea2787222020-11-24T21:18:06ZengFrontiers Media S.A.Frontiers in Genetics1664-80212019-03-011010.3389/fgene.2019.00214420104Recent Advances of Deep Learning in Bioinformatics and Computational BiologyBinhua Tang0Binhua Tang1Zixiang Pan2Kang Yin3Asif Khateeb4Epigenetics & Function Group, Hohai University, Nanjing, ChinaSchool of Public Health, Shanghai Jiao Tong University, Shanghai, ChinaEpigenetics & Function Group, Hohai University, Nanjing, ChinaEpigenetics & Function Group, Hohai University, Nanjing, ChinaEpigenetics & Function Group, Hohai University, Nanjing, ChinaExtracting 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.https://www.frontiersin.org/article/10.3389/fgene.2019.00214/fullcomputational biologybioinformaticsapplicationalgorithmdeep learning |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Binhua Tang Binhua Tang Zixiang Pan Kang Yin Asif Khateeb |
| spellingShingle |
Binhua Tang Binhua Tang Zixiang Pan Kang Yin Asif Khateeb Recent Advances of Deep Learning in Bioinformatics and Computational Biology Frontiers in Genetics computational biology bioinformatics application algorithm deep learning |
| author_facet |
Binhua Tang Binhua Tang Zixiang Pan Kang Yin Asif Khateeb |
| author_sort |
Binhua Tang |
| title |
Recent Advances of Deep Learning in Bioinformatics and Computational Biology |
| title_short |
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_sort |
recent advances of deep learning in bioinformatics and computational biology |
| publisher |
Frontiers Media S.A. |
| series |
Frontiers in Genetics |
| issn |
1664-8021 |
| publishDate |
2019-03-01 |
| 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. |
| topic |
computational biology bioinformatics application algorithm deep learning |
| url |
https://www.frontiersin.org/article/10.3389/fgene.2019.00214/full |
| work_keys_str_mv |
AT binhuatang recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology AT binhuatang recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology AT zixiangpan recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology AT kangyin recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology AT asifkhateeb recentadvancesofdeeplearninginbioinformaticsandcomputationalbiology |
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