{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["39(5)"],"submitter":["Tagashira M"],"pubmed_abstract":["<h4>Motivation</h4>To capture structural homology in RNAs, alignment and folding (AF) of RNA homologs has been a fundamental framework around RNA science. Learning sufficient scoring parameters for simultaneous AF (SAF) is an undeveloped subject because evaluating them is computationally expensive.<h4>Results</h4>We developed ConsTrain-a gradient-based machine learning method for rich SAF scoring. We also implemented ConsAlign-a SAF tool composed of ConsTrain's learned scoring parameters. To aim for better AF quality, ConsAlign employs (1) transfer learning from well-defined scoring models and (2) the ensemble model between the ConsTrain model and a well-established thermodynamic scoring model. Keeping comparable running time, ConsAlign demonstrated competitive AF prediction quality among "],"journal":["Bioinformatics (Oxford, England)"],"pagination":["btad255"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10172041"],"repository":["biostudies-literature"],"pubmed_title":["ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring."],"pmcid":["PMC10172041"],"pubmed_authors":["Tagashira M"],"additional_accession":[]},"is_claimable":false,"name":"ConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring.","description":"<h4>Motivation</h4>To capture structural homology in RNAs, alignment and folding (AF) of RNA homologs has been a fundamental framework around RNA science. Learning sufficient scoring parameters for simultaneous AF (SAF) is an undeveloped subject because evaluating them is computationally expensive.<h4>Results</h4>We developed ConsTrain-a gradient-based machine learning method for rich SAF scoring. We also implemented ConsAlign-a SAF tool composed of ConsTrain's learned scoring parameters. To aim for better AF quality, ConsAlign employs (1) transfer learning from well-defined scoring models and (2) the ensemble model between the ConsTrain model and a well-established thermodynamic scoring model. Keeping comparable running time, ConsAlign demonstrated competitive AF prediction quality among ","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 May","modification":"2025-04-26T14:27:04.155Z","creation":"2025-04-06T14:34:43.821Z"},"accession":"S-EPMC10172041","cross_references":{"pubmed":["37074925"],"doi":["10.1093/bioinformatics/btad255"]}}