DECODING SYNONYMOUS CODON SELECTION WITH A TRANSFORMER MODEL
The paper develops a transformer model to predict codon sequences from amino acids, improving rare codon prediction and linking sequence variation to protein function.
Author
Centre National de la Recherche ScientifiqueORCID 0000-0003-2899-3625Unclaimed ORCID profile · follow to get notified of new preprints
3 papers
Recent·Most cited
The paper develops a transformer model to predict codon sequences from amino acids, improving rare codon prediction and linking sequence variation to protein function.
Hélène Bret, Jinmei Gao, Diego Javier Zea…·Raphaël Guérois
The paper demonstrates that AlphaFold2-Multimer can identify interaction sites in intrinsically disordered regions with improved accuracy using fragment-based approaches and evolutionary information integration.
This paper introduces CARNAGE, a graph neural network that embeds RNA structure into sequence-like representations, enabling structural-aware sequence analysis without explicit alignment.