The DOI matches its Crossref record; 136 of the 136 references it cites were found in a registry or an index.
Current progress and open challenges for applying deep learning across the biosciences
Open accessAbstract
Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.
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Cites
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The application of artificial intelligence to biology and neuroscience
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ProkBERT family: genomic language models for microbiome applications
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“The segment databases was created by sampling fixed lengths of [256, 512, 1024] or, in other instances, variable lengths aiming for an approximate coverage of 1.”
Method
Metadata
- Authors
- Sapoval, N., Aghazadeh, A., Nute, M. G., Antunes, D. A., Balaji, A., Baraniuk, R., Barberan, C. J., Dannenfelser, R., Dun, C., Edrisi, M., Elworth, R. A. L., Kille, B., Kyrillidis, A., Nakhleh, L., Wolfe, C. R., Yan, Z., Yao, V., & Treangen, T. J.
- Venue
- Nature Communications
- Published
- 2022-04-01
- Volume
- 13
- Issue
- 1
- Pages
- 1728-1728
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W4220685469
- Cited by
- 359
- References
- 136
- FWCI
- 20.51
- Type
- journal article
- Language
- en
- Open access
- gold
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- Sapoval, N., Aghazadeh, A., Nute, M. G., Antunes, D. A., Balaji, A., Baraniuk, R., Barberan, C. J., Dannenfelser, R., Dun, C., Edrisi, M., Elworth, R. A. L., Kille, B., Kyrillidis, A., Nakhleh, L., Wolfe, C. R., Yan, Z., Yao, V., & Treangen, T. J. (2022). Current progress and open challenges for applying deep learning across the biosciences. Nature Communications. https://doi.org/10.1038/s41467-022-29268-7
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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