Deep Learning

RNA Secondary Structure Prediction by Conducting Multi-Class Classifications
Generating valid predictions of RNA secondary structures is challenging. Several deep learning methods have been developed for …
Jiyuan Yang, Kengo Sato, Martin Loza, Sung-Joon Park, Kenta Nakai
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Direct inference of base-pairing probabilities with neural networks improves prediction of RNA secondary structures with pseudoknots
Existing approaches to predicting RNA secondary structures depend on how the secondary structure is decomposed into substructures, that …
Manato Akiyama, Yasubumi Sakakibara, Kengo Sato
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RNA secondary structure prediction using deep learning with thermodynamic integration
Accurate predictions of RNA secondary structures can help uncover the roles of functional non-coding RNAs. Although machine …
Kengo Sato, Manato Akiyama, Yasubumi Sakakibara
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