Decoding functional proteome information in model organisms using protein language models.
NAR Genom Bioinform
; 6(3): lqae078, 2024 Sep.
Article
in En
| MEDLINE
| ID: mdl-38962255
ABSTRACT
Protein language models have been tested and proved to be reliable when used on curated datasets but have not yet been applied to full proteomes. Accordingly, we tested how two different machine learning-based methods performed when decoding functional information from the proteomes of selected model organisms. We found that protein language models are more precise and informative than deep learning methods for all the species tested and across the three gene ontologies studied, and that they better recover functional information from transcriptomic experiments. The results obtained indicate that these language models are likely to be suitable for large-scale annotation and downstream analyses, and we recommend a guide for their use.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Language:
En
Journal:
NAR Genom Bioinform
Year:
2024
Document type:
Article
Affiliation country:
Spain