{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Zheng D"],"funding":["National Natural Science Foundation of China"],"pagination":["btae378"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11219481"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["40(7)"],"pubmed_abstract":["<h4>Motivation</h4>ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members.<h4>Results</h4>We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in train"],"journal":["Bioinformatics (Oxford, England)"],"pubmed_title":["A deep learning method to predict bacterial ADP-ribosyltransferase toxins."],"pmcid":["PMC11219481"],"funding_grant_id":["31970635"],"pubmed_authors":["Yang J","Zhou S","Chen L","Zheng D","Pang G"],"additional_accession":[]},"is_claimable":false,"name":"A deep learning method to predict bacterial ADP-ribosyltransferase toxins.","description":"<h4>Motivation</h4>ADP-ribosylation is a critical modification involved in regulating diverse cellular processes, including chromatin structure regulation, RNA transcription, and cell death. Bacterial ADP-ribosyltransferase toxins (bARTTs) serve as potent virulence factors that orchestrate the manipulation of host cell functions to facilitate bacterial pathogenesis. Despite their pivotal role, the bioinformatic identification of novel bARTTs poses a formidable challenge due to limited verified data and the inherent sequence diversity among bARTT members.<h4>Results</h4>We proposed a deep learning-based model, ARTNet, specifically engineered to predict bARTTs from bacterial genomes. Initially, we introduced an effective data augmentation method to address the issue of data scarcity in train","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jul","modification":"2025-04-04T23:55:30.699Z","creation":"2025-04-04T23:55:30.699Z"},"accession":"S-EPMC11219481","cross_references":{"pubmed":["38885365"],"doi":["10.1093/bioinformatics/btae378"]}}