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Manato Akiyama

Showing results (1-10 of 10) with videos related to

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Methods in Molecular Biology (Clifton, N.J.)|January 27, 2023
RNA Secondary Structure Prediction Based on Energy ModelsManato Akiyama, Kengo Sato
NAR Genomics and Bioinformatics|February 25, 2022
Informative RNA base embedding for RNA structural alignment and clustering by deep representation learningManato Akiyama, Yasubumi Sakakibara
Journal of Cheminformatics|August 23, 2024
Deep learning of multimodal networks with topological regularization for drug repositioningYuto Ohnuki, Manato Akiyama, Yasubumi Sakakibara
Genes|November 24, 2022
Direct Inference of Base-Pairing Probabilities with Neural Networks Improves Prediction of RNA Secondary Structures with PseudoknotsManato Akiyama, Yasubumi Sakakibara, Kengo Sato
Journal of Bioinformatics and Computational Biology|January 9, 2019
A max-margin training of RNA secondary structure prediction integrated with the thermodynamic modelManato Akiyama, Kengo Sato, Yasubumi Sakakibara
Nature Communications|February 12, 2021
RNA secondary structure prediction using deep learning with thermodynamic integrationKengo Sato, Manato Akiyama, Yasubumi Sakakibara
Communications Chemistry|August 6, 2025
Leveraging tree-transformer VAE with fragment tokenization for high-performance large chemical model generationTensei Inukai, Aoi Yamato, Manato Akiyama, et al.
Bioinformatics Advances|January 26, 2023
Genomic style: yet another deep-learning approach to characterize bacterial genome sequencesYuka Yoshimura, Akifumi Hamada, Yohann Augey, et al.
Nature Communications|March 3, 2026
Cross-species gene redesign leveraging ortholog information and generative modelingManato Akiyama, Motohiko Tashiro, Ying Huang, et al.
Communications Chemistry|November 17, 2023
Variational autoencoder-based chemical latent space for large molecular structures with 3D complexityToshiki Ochiai, Tensei Inukai, Manato Akiyama, et al.
Pageof 1

Showing results (1-10 of 10) with videos related to

Sort By:
Pageof 1
Methods in Molecular Biology (Clifton, N.J.)|January 27, 2023
RNA Secondary Structure Prediction Based on Energy ModelsManato Akiyama, Kengo Sato
NAR Genomics and Bioinformatics|February 25, 2022
Informative RNA base embedding for RNA structural alignment and clustering by deep representation learningManato Akiyama, Yasubumi Sakakibara
Journal of Cheminformatics|August 23, 2024
Deep learning of multimodal networks with topological regularization for drug repositioningYuto Ohnuki, Manato Akiyama, Yasubumi Sakakibara
Genes|November 24, 2022
Direct Inference of Base-Pairing Probabilities with Neural Networks Improves Prediction of RNA Secondary Structures with PseudoknotsManato Akiyama, Yasubumi Sakakibara, Kengo Sato
Journal of Bioinformatics and Computational Biology|January 9, 2019
A max-margin training of RNA secondary structure prediction integrated with the thermodynamic modelManato Akiyama, Kengo Sato, Yasubumi Sakakibara
Nature Communications|February 12, 2021
RNA secondary structure prediction using deep learning with thermodynamic integrationKengo Sato, Manato Akiyama, Yasubumi Sakakibara
Communications Chemistry|August 6, 2025
Leveraging tree-transformer VAE with fragment tokenization for high-performance large chemical model generationTensei Inukai, Aoi Yamato, Manato Akiyama, et al.
Bioinformatics Advances|January 26, 2023
Genomic style: yet another deep-learning approach to characterize bacterial genome sequencesYuka Yoshimura, Akifumi Hamada, Yohann Augey, et al.
Nature Communications|March 3, 2026
Cross-species gene redesign leveraging ortholog information and generative modelingManato Akiyama, Motohiko Tashiro, Ying Huang, et al.
Communications Chemistry|November 17, 2023
Variational autoencoder-based chemical latent space for large molecular structures with 3D complexityToshiki Ochiai, Tensei Inukai, Manato Akiyama, et al.
Pageof 1