{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["5(1)"],"submitter":["Clauwaert J"],"funding":["Ghent University","Novo Nordisk employees","Novo Nordisk Research Centre Oxford Ltd"],"pubmed_abstract":["The correct mapping of the proteome is an important step towards advancing our understanding of biological systems and cellular mechanisms. Methods that provide better mappings can fuel important processes such as drug discovery and disease understanding. Currently, true determination of translation initiation sites is primarily achieved by <i>in vivo</i> experiments. Here, we propose TIS Transformer, a deep learning model for the determination of translation start sites solely utilizing the information embedded in the transcript nucleotide sequence. The method is built upon deep learning techniques first designed for natural language processing. We prove this approach to be best suited for learning the semantics of translation, outperforming previous approaches by a large margin. We demon"],"journal":["NAR genomics and bioinformatics"],"pagination":["lqad021"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9985340"],"repository":["biostudies-literature"],"pubmed_title":["TIS Transformer: remapping the human proteome using deep learning."],"pmcid":["PMC9985340"],"pubmed_authors":["Gupta R","Menschaert G","Clauwaert J","McVey Z"],"additional_accession":[]},"is_claimable":false,"name":"TIS Transformer: remapping the human proteome using deep learning.","description":"The correct mapping of the proteome is an important step towards advancing our understanding of biological systems and cellular mechanisms. Methods that provide better mappings can fuel important processes such as drug discovery and disease understanding. Currently, true determination of translation initiation sites is primarily achieved by <i>in vivo</i> experiments. Here, we propose TIS Transformer, a deep learning model for the determination of translation start sites solely utilizing the information embedded in the transcript nucleotide sequence. The method is built upon deep learning techniques first designed for natural language processing. We prove this approach to be best suited for learning the semantics of translation, outperforming previous approaches by a large margin. We demon","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Mar","modification":"2025-04-04T11:35:16.079Z","creation":"2025-02-18T23:35:19.922Z"},"accession":"S-EPMC9985340","cross_references":{"pubmed":["36879896"],"doi":["10.1093/nargab/lqad021"]}}