May 14, 2024, 4:43 a.m. | Aleix Lafita, Ferran Gonzalez, Mahmoud Hossam, Paul Smyth, Jacob Deasy, Ari Allyn-Feuer, Daniel Seaton, Stephen Young

cs.LG updates on

arXiv:2405.06729v1 Announce Type: cross
Abstract: Protein Language Models (PLMs) have emerged as performant and scalable tools for predicting the functional impact and clinical significance of protein-coding variants, but they still lag experimental accuracy. Here, we present a novel fine-tuning approach to improve the performance of PLMs with experimental maps of variant effects from Deep Mutational Scanning (DMS) assays using a Normalised Log-odds Ratio (NLR) head. We find consistent improvements in a held-out protein test set, and on independent DMS and …

abstract accuracy arxiv clinical coding cs.lg experimental fine-tuning functional impact language language models novel performance prediction protein scalable significance tools type variants

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