deep-review
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greenelab
A collaboratively written review paper on deep learning, genomics, and precision medicine
Many papers on mass-spectrometry based proteomics have shown the use of deep learning for intensity prediction, combining scores across search algorithms, retention time, collision corss section prediction etc. It is...
Version 2.0 should include Alpha Fold 2. This method and the CASP14 results may even lead us to reconsider broader statements, like this part of the abstract: > Following from...
> A lack of tools to precisely control gene expression has limited our ability to evaluate relationships between expression levels and phenotypes. Here, we describe an approach to titrate expression...
So, what do you think about doing some quantitative analysis publications? E.g. look at the number of abstracts that use deep learning in particular domains? Count the number of authors?...
Please: consider this a draft to see the direction I'm taking. Help would be very welcome! While writing, I noticed that the restructuring that was needed was more extensive than...
Hi, I looked at the changes I made and tweaked them. I added some new references (marked with @doi and @arxiv tags). I hope updating the tags to the new...
I suggest to include "Human Protein Atlas (HPA), which aims to map all the proteins in the human body. Research is focused on protein science, understanding the biology and functions...
> To enable large-scale analyses of regulatory logic in model species, we developed DeepArk (https://DeepArk.princeton.edu), a set of deep learning models of the cis-regulatory codes of four widely-studied species: Caenorhabditis...
> Next-generation sequencing (NGS) technologies have enabled affordable sequencing of billions of short DNA fragments at high throughput, paving the way for population-scale genomics. Genomics data analytics at this scale...