Deep learning models for sequential data can be trained to make accurate predictions from large biological datasets. New tools from computer vision and natural language processing can help us make these models interpretable to biologists.
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References
Sample, P. J. et al. Nat. Biotechnol. 37, 803–809 (2019).
Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Nat. Biotechnol. 33, 831–838 (2015).
Senior, A. W. et al. Nature 577, 706–710 (2020).
Linder, J. et al. Nat. Mach. Intell. 4, 41–54 (2022).
Chen, J., Song, L., Wainwright, M. & Jordan, M. Int. Conf. Machine Learning (883–892 (2018).
Yang, J. et al. Proc. Natl. Acad. Sci. USA 117, 4196–1503 (2020).
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Alaa, A.M. Mining for informative signals in biological sequences. Nat Mach Intell 4, 665–666 (2022). https://doi.org/10.1038/s42256-022-00524-1
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DOI: https://doi.org/10.1038/s42256-022-00524-1
