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Plos One|October 24, 2022
ICAN: Interpretable cross-attention network for identifying drug and target protein interactionsHiroyuki Kurata, Sho Tsukiyama
Computational and Structural Biotechnology Journal|October 17, 2022
Cross-attention PHV: Prediction of human and virus protein-protein interactions using cross-attention-based neural networksSho Tsukiyama, Hiroyuki Kurata
Briefings in Bioinformatics|June 30, 2022
iACVP: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec modelHiroyuki Kurata, Sho Tsukiyama, Balachandran Manavalan
Computational and Structural Biotechnology Journal|January 20, 2023
CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site predictionSho Tsukiyama, Md Mehedi Hasan, Hiroyuki Kurata
Briefings in Bioinformatics|June 23, 2021
LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vecSho Tsukiyama, Md Mehedi Hasan, Satoshi Fujii, et al.
Briefings in Bioinformatics|February 28, 2022
BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approachesSho Tsukiyama, Md Mehedi Hasan, Hong-Wen Deng, et al.
Plos One|August 22, 2024
PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptidesHiroyuki Kurata, Md Harun-Or-Roshid, Sho Tsukiyama, et al.
Plos Biology|February 17, 2026
DRfold2 is a deep learning-based tool that enables efficient and accurate RNA structure predictionYang Li, Chenjie Feng, Xi Zhang, et al.
Methods (San Diego, Calif.)|May 10, 2024
MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning modelsHiroyuki Kurata, Md Harun-Or-Roshid, Md Mehedi Hasan, et al.
Molecular Therapy : the Journal of the American Society of Gene Therapy|May 8, 2022
Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategyMd Mehedi Hasan, Sho Tsukiyama, Jae Youl Cho, et al.
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