首页|Decoding proteome functional information in model organisms using protein langua ge models
Decoding proteome functional information in model organisms using protein langua ge models
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-According to news reporting based on a preprint abstract, our journalists obtained the following quote sourced from bi orxiv.org: "The gap between sequence information and experimental determination of function in proteins is currently unsolvable. The computational and automatic implementa tion of function prediction is not significantly improving function assignment, so there is a pressing need to find alternative methods, since standard approach es are not able to bridge the gap. Modern machine learning methods have been rec ently developed to predict function, being deep neural convolutional networks a popular choice. Protein language models have been also tested and proved reliabl e in curated datasets, but have not been applied yet to full collections of cura ted proteomes. "We have tested how two different machine learning based methods perform when de coding the functional information from proteomes of selected model organisms. "We found that the protein Language Models are more precise and informative acro ss gene ontology categories for all the species, recovering functional informati on from transcriptomics experiments.
BioinformaticsBiotechnologyBiotechno logy - BioinformaticsCyborgsEmerging TechnologiesInformation TechnologyM achine LearningPeptides and ProteinsProteinsProteome
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