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Keyword: long sequence modeling ×Clear all

2 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   ProteinBERT: a universal deep-learning model of protein sequence and function

   Brandes, Ofer, Peleg, Rappoport and Linial · Bioinformatics · 2022

   The authors introduce ProteinBERT, a language model built for proteins that adds Gene Ontology annotation prediction to its pretraining and handles long sequences efficiently.

   Unchecked2 claims
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   1. UncheckedProteinBERT reaches near state-of-the-art results, sometimes better, on several protein benchmarks while being a much smaller and faster model.“ProteinBERT obtains near state-of-the-art performance, and sometimes exceeds it, on multiple benchmarks covering diverse protein properties (including protein structure, post-translational modifications and biophysical attributes), despite using a far smaller and faster model than competing deep-le…”
   2. UncheckedThe authors say ProteinBERT offers an efficient way to train protein predictors quickly, even when little labelled data is available.“Overall, ProteinBERT provides an efficient framework for rapidly training protein predictors, even with limited labeled data.”