biostudies-literatureUnknown39(5)Tagashira M<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 Bioinformatics (Oxford, England)btad255https://www.ebi.ac.uk/biostudies/studies/S-EPMC10172041biostudies-literatureConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring.PMC10172041Tagashira MfalseConsAlign: simultaneous RNA structural aligner based on rich transfer learning and thermodynamic ensemble model of alignment scoring.<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 2023-01-01T00:00:00Z2023 May2025-04-26T14:27:04.155Z2025-04-06T14:34:43.821ZS-EPMC101720413707492510.1093/bioinformatics/btad255