Decoding functional proteome information in model organisms using protein language models
Decoding functional proteome information in model organisms using protein language models
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 transcriptomics 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.
Fern¨￠ndez Rosa、Medina-Burgos Patricia、Mart¨anez-Redondo Gemma I.、Barrios-N¨2?ez Israel、Rojas Ana M.、Cases Ildefonso
Metazoa Phylogenomics Lab, Institute of Evolutionary Biology (CSIC-UPF)Computational Biology and Bioinformatics group, Andalusian Center for Developmental Biology (CABD-CSIC)Metazoa Phylogenomics Lab, Institute of Evolutionary Biology (CSIC-UPF)Computational Biology and Bioinformatics group, Andalusian Center for Developmental Biology (CABD-CSIC)Computational Biology and Bioinformatics group, Andalusian Center for Developmental Biology (CABD-CSIC)Bioinformatics Unit, Andalusian Center for Developmental Biology (CABD-CSIC)
生物科学研究方法、生物科学研究技术分子生物学生物化学
Fern¨￠ndez Rosa,Medina-Burgos Patricia,Mart¨anez-Redondo Gemma I.,Barrios-N¨2?ez Israel,Rojas Ana M.,Cases Ildefonso.Decoding functional proteome information in model organisms using protein language models[EB/OL].(2025-03-28)[2025-04-26].https://www.biorxiv.org/content/10.1101/2024.02.14.580341.点此复制
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