ProteinGLUE multi-task benchmark suite for self-supervised protein modeling.

ABSTRACT: Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties. We introduce the ProteinGLUE benchmark for the evaluation of protein representations: a set of seven per-amino-acid tasks for evaluating learned protein representations. We also offer reference code, and we provide two baseline models with hyperparameters specifically trained for these benchmarks. Pre-training was done on two tasks, masked symbol prediction and next sentence prediction. We show that pre-training yields higher perf

SUBMITTER: Capel H 

PROVIDER: S-EPMC9512797 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature