Can someone review relevant papers published in the last year?

[Benjamin-Lee]

I'm happy to do this myself but would prefer to delegate if at all possible. Looking at the commit graph, it seems this project slowed down about a year ago.

As mentioned in https://github.com/Benjamin-Lee/deep-rules/issues/205#issuecomment-638226596, we need to add references to any new papers with relevant information. Does anyone want to take this on? It's one of the last steps we need to do before submitting.

[tbrittoborges]

• Predicting Splicing from Primary Sequence with Deep Learning
• A Deep Neural Network for Predicting and Engineering Alternative Polyadenylation
• Single-cell RNA-seq denoising using a deep count autoencoder

[evancofer]

This is a pretty daunting task, and could be worthy of its own review paper. I'm not sure if we need to do something this extensive either, simply because many of these papers may be focused on straightforward applications of deep learning rather than changing how we apply deep learning to computational biology. I think this may be why this issue hasn't really gotten much of a response.

That being said, there are a lot of recent review papers that might be good starting points for this (e.g. https://doi.org/10.1038/s41576-019-0122-6 or https://doi.org/10.1016/j.csbj.2020.06.017 )