Recent advances of deep learning in bioinformatics and computational biology
Open accessAbstract
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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Deep neural networks have been widely used to analyse concatenated multi-omics data.
“Other than classical ML algorithms, deep neural networks (Tang et al., 2019) have also been widely used to analyse concatenated multi-omics data.”
From Using machine learning approaches for multi-omics data analysis: A review (2021) · cites this paper as Tang et al., 2019
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References 49 · Cited by 233
Research works printed but not linked: 1
- Xu T. Zhang H. Huang X. Zhang S. Metaxas D. N. (2016). Multimodal deep learning for cervical dysplasia diagnosis, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Boston, MA), 115– 123.
- Authors
- Tang, B., Pan, Z., Yin, K., & Khateeb, A.
- Affiliations
- Hohai University (CN)
- Corresponding author
- Tang, Binhua
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2019-03-26
- Volume
- 10
- Pages
- 214-214
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC6443823
- OpenAlex
- W2922701384
- Cited by
- 233
- References
- 49
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 33 · 2023: 44 · 2022: 49 · 2021: 50 · 2020: 30 · 2019: 5
- FWCI
- 2.95
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation (BK20161196, BE2016655)
- Gordon and Betty Moore Foundation (BE2016655, BK20161196)
- National Natural Science Foundation of China
- Fundamental Research Funds for the Central Universities
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- deep learning, omics data, bioinformatics applications, deep learning limitations
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