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Blind Prediction of Complex Water and Ion Ensembles Around RNA in CASP16
ABSTRACT Biomolecules rely on water and ions for stable folding, but these interactions are often transient, dynamic, or disordered and …
Rachael C. Kretsch, Elisa Posani, Eugene F. Baulin, Janusz M. Bujnicki, Giovanni Bussi, Thomas E. Cheatham III, Shi-Jie Chen, Arne Elofsson, Masoud Amiri Farsani, Olivia N. Fisher, M. Michael Gromiha, Ayush Gupta, Michiaki Hamada, K. Harini, Gang Hu, David Huang, Junichi Iwakiri, Anika Jain, Yuki Kagaya, Daisuke Kihara, Sebastian Kmiecik, Sowmya Ramaswamy Krishnan, Ikuo Kurisaki, Olivier Languin-Cattoën, Jun Li, Shanshan Li, Karim Malekzadeh, Tsukasa Nakamura, Wentao Ni, Chandran Nithin, Michael Z. Palo, Joon Hong Park, Smita P. Pilla, Simón Poblete, Fabrizio Pucci, Pranav Punuru, Anouka Saha, Kengo Sato, Ambuj Srivastava, Genki Terashi, Emilia Tugolukova, Jacob Verburgt, Qiqige Wuyun, Gül H. Zerze, Kaiming Zhang, Sicheng Zhang, Wei Zheng, Yuanzhe Zhou, Wah Chiu, David A. Case, Rhiju Das
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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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A functional connection between the Microprocessor and a variant NEXT complex
In mammalian cells, primary miRNAs are cleaved at their hairpin structures by the Microprocessor complex, whose core is composed of …
Katsutoshi Imamura, William Garland, Manfred Schmid, Lis Jakobsen, Kengo Sato, Jérôme O Rouvière, Kristoffer Pors Jakobsen, Elena Burlacu, Marta Loureiro Lopez, Søren Lykke-Andersen, Jens S Andersen, Torben Heick Jensen
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Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery
Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life …
Kengo Sato, Michiaki Hamada
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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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Integer programming for selecting set of informative markers in paternity inference
BACKGROUND: Parentage information is fundamental to various life sciences. Recent advances in sequencing technologies have made it …
Soichiro Nishiyama, Kengo Sato, Ryutaro Tao
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Prediction of RNA secondary structure including pseudoknots for long sequences
RNA structural elements called pseudoknots are involved in various biological phenomena including ribosomal frameshifts. Because it is …
Kengo Sato, Yuki Kato
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A max-margin model for predicting residue-base contacts in protein-RNA interactions
Protein-RNA interactions (PRIs) are essential for many biological processes, so understanding aspects of the sequences and structures …
Shunya Kashiwagi, Kengo Sato, Yasubumi Sakakibara
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A web server for designing molecular switches composed of two interacting RNAs
The programmability of RNA-RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of …
Akito Taneda, 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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