Author

Sho Tsukiyama

Kyushu Institute of TechnologyORCID 0000-0002-7871-0868Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3 Bioengineering· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Research Focus:protein structure prediction· 3 Organism:human· 2 Assay:protein sequence· 2 Organism:virus· 2 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Approach:contrastive learning· 1

Frequent Co-Authors

Hiroyuki KurataiD· 4 shared Satoshi FujiiiD· 2 shared Kengo SatoiD· 1 shared Md Mehedi HasaniD· 1 shared Saori Kudo· 1 shared Sei‐ichiro KamataiD· 1 shared Shunsuke Aoki· 1 shared Tatsuro Yata· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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4 shared papersBioinformatics

Hiroyuki KurataORCID

Mutual co-authors in cluster

Author profile →

5 papers

Recent·Most cited

RNArefine: AI-guided Atomic-Level Refinement of RNA Structures

Sho Tsukiyama, Yang Li, Kengo Sato, Hiroyuki Kurata, Yang Zhang

RNArefine improves RNA structure prediction by refining atomic details and stereochemistry using AI and physics-based methods.

bioRxiv · Jun 29, 2026 · PreprintBioengineering

Mora-ERP-based RNN-Transformer for decoding single-trial EEGs during silent Japanese speeches

Toshimasa Yamazaki, Saori Kudo, Sei‐ichiro Kamata, Satoshi Fujii, Sho Tsukiyama, Tatsuro Yata, Shunsuke Aoki

This paper develops an RNN-Transformer model to decode single-trial EEGs during silent Japanese speeches with low error rates.

bioRxiv · Nov 18, 2024 · PreprintNeuroscience

ICAN: interpretable cross-attention network for identifying drug and target protein interactions

Hiroyuki Kurata, Sho Tsukiyama

The paper introduces ICAN, an attention-based method for predicting drug-target interactions using SMILES and amino acid sequences, achieving high performance and interpretability.

PLoS ONE · Oct 24, 2022Peer-reviewedBioinformatics

Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention-based neural networks

Sho Tsukiyama, Hiroyuki Kurata

The paper introduces cross-attention PHV, a computational predictor for human-virus protein-protein interactions, which outperforms existing models and accurately predicts interactions for unknown viruses.

Computational and Structural Biotechnology Journal · Jan 1, 2022Peer-reviewedBioinformatics

LSTM-PHV: Prediction of human-virus protein-protein interactions by LSTM with word2vec

Sho Tsukiyama, Md Mehedi Hasan, Satoshi Fujii, Hiroyuki Kurata

The paper introduces LSTM-PHV, a sequence-based model predicting human-virus protein-protein interactions using LSTM and word2vec, achieving high accuracy on imbalanced data.

Briefings in Bioinformatics · May 25, 2021Peer-reviewedBioinformatics