Benjamin-Lee
A primer on deep learning in genomics
https://doi.org/10.1038/s41588-018-0295-5
Deep learning methods are a class of machine learning techniques capable of identifying highly complex patterns in large datasets. Here, we provide a perspective and primer on deep learning applications for genome analysis. We discuss successful applications in the fields of regulatory genomics, variant calling and pathogenicity scores. We include general guidance for how to effectively use deep learning methods as well as a practical guide to tools and resources. This primer is accompanied by an interactive online tutorial.
This primer isn't in a ten simple rules format, but there is some overlap we with the goals of this project. We should review it and potentially use it as a reference. For instance, Table 1 lists resources for #31.
There is another recent review ( https://doi.org/10.1038/nbt.4233 ) that I found to be relevant. Perhaps we could list these works in one place, or would it be better to just make an issue for each one?
@evancofer my personal preference would be to create a new issue for each review we want to cite. That way we can briefly note why we want to include that particular review instead of others.
Another feature of the primer I linked above that could be informative for our target audience is the tutorial notebook.
@agitter That is a nice touch. I think it could be interesting to implement a similar notebook for this paper, and show a sort of step-by-step DL application that uses all of the 10 rules.
@evancofer that would be cool! Want to open a new issue to discuss what that would look like?