Thanks. Happy to help if you run into any issues. If the goal is differential signal prediction, would need a slightly different fine tuning set up for optimal performance

Should have used this!

And just to be clear, I have no specific comments on the other primary models implemented in the CRESTED package including the ones they developed. They look good from the paper. I'm simply stating that the "Dilated CNN" implementation in the package is NOT ChromBPNet. 4/4

Here's the link to the system, try it! qedscience.com
@qedscience.bsky.social

Thanks

Came across this interesting paper www.nature.com/articles/s41... that compares their model to a "Dilated CNN" model from the CRESTED package that is supposed to "ChromBPNet inspired". Performance of the dilated CNN is miserable. This is categorically NOT a ChromBPNet model 3/3

Please don't refer to models from other packages as ChromBPNet. There is much more to ChromBPNet than the architecture. The training procedure, bias correction & data sampling strategy has a huge impact on performance. 2/

And I think this is just the tip of the iceberg. There will be many ways to push this further. Really huge breakthrough IMO from my first reading. Can't wait to use this & build on it. Huge congrats to the team! 3/3

Also an absolute showcase for what self supervised learning can do when done right. IMO, this is the first paper that really shows the power of self supervised genomic language models. 2/

But lets be clear that glam journal publishing is a branding & marketing exercise. It is not in any way a step towards greater transparency, accountability & credibility. 12/12