ArticleNAR genomics and bioinformatics2024
Decoding functional proteome information in model organisms using protein language models.
Article in NAR genomics and bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Protein Language Model Embeddings Recover Evolutionary and Functional Organization Across the Ras Superfamily.Journal of molecular evolution · 2026Article
- FANTASIA suite: a reproducible and configurable framework for embedding-based functional annotation of proteins.NAR genomics and bioinformatics · 2026Article
- Artificial intelligence in plant salt stress research: from predictive models to multi-omics integration.Journal of experimental botany · 2026Review
- FANTASIA leverages language models to decode the functional dark proteome across the animal tree of life.Communications biology · 2025Article
- Structural Changes in Gene Ontology Reveal Modular and Complex Representations of Biological Function.Molecular biology and evolution · 2025Article
- Advances and challenges in understanding evolution through genome comparison: meeting report of the European Molecular Biology Organization (EMBO) lecture course "Evolutionary and Comparative Genomics".Bioinformatics advances · 2025Article
- MATEdb2, a Collection of High-Quality Metazoan Proteomes across the Animal Tree of Life to Speed Up Phylogenomic Studies.Genome biology and evolution · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.