The key ingredient of our solution was MPRA-LegNet, but we also incorporated a large number of new ideas to master the challenge.
It’s inspiring that the second-place team also used LegNet as the basis for their solution.
More details to come
Our team achieved first place in the CAGI7 lentiMPRA challenge on predicting the effects of single-nucleotide mutations in regulatory elements, surpassing the nearest competitors by a significant margin.
Dmitry Penzar
Dmitry Penzar
New paper showing that much of the apparent success of protein language models in predicting mutational effects is a mirage: These models mostly memorize sites. 1/
www.biorxiv.org/content/10.6...
(1/14) Excited to share our new preprint: ArChIPelago — classic ML on top of multiple PWMs improves genomic TFBS prediction. shorturl.at/jnxDA
Builds on our MEX paper (Vorontsov et al., 2025).
w/ @pensarata.bsky.social
@vorontsovie.bsky.social
@sevamakeev.bsky.social
@halfacrocodile.bsky.social
