DNABERT
stanford-tensorflow-tutorials
| DNABERT | stanford-tensorflow-tutorials | |
|---|---|---|
| 1 | 2 | |
| 546 | 9,845 | |
| - | - | |
| 3.1 | 0.0 | |
| 2 months ago | over 3 years ago | |
| Python | Python | |
| Apache License 2.0 | MIT License |
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DNABERT
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[D] New to DNABERT
If I want to get started, they said it's optional to pre-train (so you can skip to step 3). This is where I got tripped up: "Note that the sequences are in kmer format, so you will need to convert your sequences into that." From what I understand, you need to do this so that all of the sequences are the same length? So kmer=6 means all of the sequences are length 6? Someone suggested that I take the first nucleotide in the promoter and grab 3 nucleotides before and 3 nucleotides after (+/-3 bases). I don't think that's how the kmer thing works though? I tried replicating how I think it works down below (I got confused on the last row of the 'after' df). Please correct me if I'm wrong!
stanford-tensorflow-tutorials
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Rim Dillon
I’m not sure how to tell you this in a way that won’t deflate your outrage boner, but Stanford uses master in code: https://github.com/chiphuyen/stanford-tensorflow-tutorials
- [D] I'm trying to do more stuff in pure Tensorflow. Is there an in-depth book that explain constructing recurrent, convolutional, graph etc layers in it?
What are some alternatives?
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