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Status: Unchecked ×Keyword: protein sequence similarity ×Clear all

2 claims from 1 paper

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

   Which pLM to choose?

   Senoner, Koludarov, Guenther, Shehu, Rost and Bromberg · bioRxiv (Cold Spring Harbor Laboratory) · 2025

   The authors benchmarked fourteen protein language models of very different sizes on how well they capture sequence, structure and function similarity, to guide choosing a model.

   Unchecked2 claims
   Show 2 claims

   1. UncheckedMid-sized protein language models matched much larger ones at reflecting every biological property tested, in this benchmark of fourteen models.“Here, a size-performance paradox is salient – mid-scale foundation models are as good as much larger ones in reflecting all tested biological properties.”
   2. UncheckedTraining a protein language model on one task reshapes its embedding space, so it grasps that task but further extractions from it become garbled.“Finally, we observe that a task-specific learning radically reshapes the embedding space, gaining inherent understanding of the task, but garbling any further extractions.”