A gene sequence-to-expression machine learning model achieves improved accuracy by incorporating information about potential long-range interactions.
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References
Avsec, Z. et al. Nat. Methods https://doi.org/10.1038/s41592-021-01252-x (2021).
Kelley, D. R. et al. Genome Res. 28, 739–750 (2018).
Ouyang, Z., Zhou, Q. & Wong, W. H. Proc. Natl Acad. Sci. USA 106, 21521–21526 (2009).
Karlić, R. et al. Proc. Natl Acad. Sci. USA 107, 2926–2931 (2010).
Kelley, D. R., Snoek, J. & Rinn, J. L. Genome Res. 26, 990–999 (2016).
Zhou, J. et al. Nat. Genet. 50, 1171–1179 (2018).
Waswani, A. et al. in Advances in Neural Information Processing Systems 30 (NIPS2017) 6000–6010 (2017).
Gasperini, M., Tome, J. M. & Shendure, J. Nat. Rev. Genet. 21, 292–310 (2020).
Fudenberg, G., Kelley, D. R. & Pollard, K. S. Nat. Methods 17, 1111–1117 (2020).
Schreiber, J.M., Lu, Y.Y. & Noble, W.S. in ICML Workshop on Computational Biology (2020).
Acknowledgements
This work was supported by NIH award U01 HG009395.
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Lu, Y.Y., Noble, W.S. A wider field of view to predict expression. Nat Methods 18, 1155–1156 (2021). https://doi.org/10.1038/s41592-021-01259-4
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DOI: https://doi.org/10.1038/s41592-021-01259-4