Are genomic language models all you need? Exploring genomic language models on protein downstream tasks.

ABSTRACT:

Motivation

Large language models, trained on enormous corpora of biological sequences, are state-of-the-art for downstream genomic and proteomic tasks. Since the genome contains the information to encode all proteins, genomic language models (gLMs) hold the potential to make downstream predictions not only about DNA sequences, but also about proteins. However, the performance of gLMs on protein tasks remains unknown, due to few tasks pairing proteins with the coding DNA sequences (CDS) that can be processed by gLMs.

Results

In this work, we curated five such datasets and used them to evaluate the performance of gLMs and proteomic language models (pLMs). We show that gLMs are competitive and even outperform their pLMs counterparts on some tasks. The best performance was achie

SUBMITTER: Boshar S 

PROVIDER: S-EPMC11399231 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature