Seyone Chithrananda
@seyonec.bsky.social
trying to learn bio and computation at Berkeley
Excited to share our work on calibrated protein search and genome mining with conformal prediction, now in @naturecomms.bsky.social!
📎 rdcu.be/d5pJG
💻 github.com/ronboger/confo…
(1/3)
📎 rdcu.be/d5pJG
💻 github.com/ronboger/confo…
(1/3)
January 6, 2025 at 12:31 AM
Excited to share our work on calibrated protein search and genome mining with conformal prediction, now in @naturecomms.bsky.social!
📎 rdcu.be/d5pJG
💻 github.com/ronboger/confo…
(1/3)
📎 rdcu.be/d5pJG
💻 github.com/ronboger/confo…
(1/3)
Reposted by Seyone Chithrananda
Large protein language models can learn complex epistatic interactions, but how much does that help with predicting variant effects? In this NeurIPS article, we show that classical independent-sites phylogenetic models can outperform pLMs on this task.
1/7
openreview.net/forum?id=H7m...
1/7
openreview.net/forum?id=H7m...
Ultrafast classical phylogenetic method beats large protein...
Amino acid substitution rate matrices are fundamental to statistical phylogenetics and evolutionary biology. Estimating them typically requires reconstructed trees for massive amounts of aligned...
openreview.net
November 16, 2024 at 8:42 PM
Large protein language models can learn complex epistatic interactions, but how much does that help with predicting variant effects? In this NeurIPS article, we show that classical independent-sites phylogenetic models can outperform pLMs on this task.
1/7
openreview.net/forum?id=H7m...
1/7
openreview.net/forum?id=H7m...