Navigating the pitfalls of applying machine learning in genomics
Author:
Publisher
Springer Science and Business Media LLC
Subject
Genetics (clinical),Genetics,Molecular Biology
Link
https://www.nature.com/articles/s41576-021-00434-9.pdf
Reference113 articles.
1. Teschendorff, A. E. Avoiding common pitfalls in machine learning omic data science. Nat. Mater. 18, 422–427 (2019). This Comment article talks about cross-validation and independent test sets as solutions to two pitfalls encountered when applying supervised ML in genomics: the ‘curse of dimensionality’ and confounding.
2. Minhas, F., Asif, A. & Ben-Hur, A. Ten ways to fool the masses with machine learning. Preprint at arXiv https://arxiv.org/abs/1901.01686 (2019).
3. Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019).
4. Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).
5. Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51, 12–18 (2019).
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