Faculty Profiles - KURATA Hiroyuki

Papers - KURATA Hiroyuki

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PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides Reviewed International journal

Kurata H., Harun-Or-Roshid M., Tsukiyama S., Maeda K.

PLoS ONE 19 ( 8 ) 2024.08

MLm5C: A high-precision human RNA 5-methylcytosine sites predictor based on a combination of hybrid machine learning models Reviewed International journal

Kurata H., Harun-Or-Roshid M., Mehedi Hasan M., Tsukiyama S., Maeda K., Manavalan B.

Methods 227 37 - 47 2024.07

Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2<sup>0</sup>-O-methylation sites in human RNA Reviewed International journal

Harun-Or-Roshid M., Pham N.T., Manavalan B., Kurata H.

PLoS ONE 19 ( 6 June ) 2024.06

Stack-DHUpred: Advancing the accuracy of dihydrouridine modification sites detection via stacking approach Reviewed International journal

Harun-Or-Roshid M., Maeda K., Phan L.T., Manavalan B., Kurata H.

Computers in Biology and Medicine 169 2024.02

Elucidating Key Characteristics of PFAS Binding to Human Peroxisome Proliferator-Activated Receptor Alpha: An Explainable Machine Learning Approach Reviewed International journal

Maeda K., Hirano M., Hayashi T., Iida M., Kurata H., Ishibashi H.

Environmental Science and Technology 58 ( 1 ) 488 - 497 2024.01

CNN6mA: Interpretable neural network model based on position-specific CNN and cross-interactive network for 6mA site prediction Reviewed International journal

Tsukiyama S., Hasan M.M., Kurata H.

Computational and Structural Biotechnology Journal 21 644 - 654 2023.01

MLAGO: machine learning-aided global optimization for Michaelis constant estimation of kinetic modeling Reviewed International journal

Maeda K., Hatae A., Sakai Y., Boogerd F.C., Kurata H.

BMC Bioinformatics 23 ( 1 ) 2022.12

Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy Reviewed International journal

Hasan M.M., Tsukiyama S., Cho J.Y., Kurata H., Alam M.A., Liu X., Manavalan B., Deng H.W.

Molecular Therapy 30 ( 8 ) 2856 - 2867 2022.08

IACVP: Markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model Reviewed International journal

Kurata H., Tsukiyama S., Manavalan B.

Briefings in Bioinformatics 23 ( 4 ) 2022.07

BERT6mA: Prediction of DNA N6-methyladenine site using deep learning-based approaches Reviewed International journal

Tsukiyama S., Hasan M.M., Deng H.W., Kurata H.

Briefings in Bioinformatics 23 ( 2 ) 2022.03

TOPICS 医用工学・医療情報学世界初バーチャルヒト全身代謝モデルの開発と糖尿病への応用 Reviewed

倉田博之

医学のあゆみ 280 ( 9 ) 948 - 949 2022.02

Identification of a reliable sacral-sparing examination to assess the ASIA impairment scale in patients with traumatic spinal cord injury Reviewed International journal

Ariji Y., Hayashi T., Ideta R., Koga R., Murai S., Naka T., Ifuku R., Towatari F., Sakai H., Kurata H., Maeda T.

Journal of Spinal Cord Medicine 2022.01

Automatic Generation of SBML Kinetic Models from Natural Language Texts Using GPT Reviewed International journal

Maeda K., Kurata H.

International Journal of Molecular Sciences 24 ( 8 ) 2023.04

ICAN: Interpretable cross-attention network for identifying drug and target protein interactions Reviewed International journal

Kurata H., Tsukiyama S.

PLoS ONE 17 ( 10 October ) 2022.10

Recent Development of Bioinformatics Tools for microRNA Target Prediction Invited Reviewed International journal

Khatun M.S., Alam M.A., Shoombuatong W., Mollah M.N.H., Kurata H., Hasan M.M.

Current Medicinal Chemistry 29 ( 5 ) 865 - 880 2022.02

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

Tsukiyama S., Kurata H.

Computational and Structural Biotechnology Journal 20 5564 - 5573 2022.01

NeuroPred-FRL: an interpretable prediction model for identifying neuropeptide using feature representation learning Reviewed

Hasan M.M., Alam M.A., Shoombuatong W., Deng H.W., Manavalan B., Kurata H.

Briefings in bioinformatics 22 ( 6 ) 2021.11

Authorship：Corresponding author Language：English Publishing type：Research paper (scientific journal)

Neuropeptides (NPs) are the most versatile neurotransmitters in the immune systems that regulate various central anxious hormones. An efficient and effective bioinformatics tool for rapid and accurate large-scale identification of NPs is critical in immunoinformatics, which is indispensable for basic research and drug development. Although a few NP prediction tools have been developed, it is mandatory to improve their NPs' prediction performances. In this study, we have developed a machine learning-based meta-predictor called NeuroPred-FRL by employing the feature representation learning approach. First, we generated 66 optimal baseline models by employing 11 different encodings, six different classifiers and a two-step feature selection approach. The predicted probability scores of NPs based on the 66 baseline models were combined to be deemed as the input feature vector. Second, in order to enhance the feature representation ability, we applied the two-step feature selection approach to optimize the 66-D probability feature vector and then inputted the optimal one into a random forest classifier for the final meta-model (NeuroPred-FRL) construction. Benchmarking experiments based on both cross-validation and independent tests indicate that the NeuroPred-FRL achieves a superior prediction performance of NPs compared with the other state-of-the-art predictors. We believe that the proposed NeuroPred-FRL can serve as a powerful tool for large-scale identification of NPs, facilitating the characterization of their functional mechanisms and expediting their applications in clinical therapy. Moreover, we interpreted some model mechanisms of NeuroPred-FRL by leveraging the robust SHapley Additive exPlanation algorithm.

DOI： 10.1093/bib/bbab167

Scopus

Other Link： https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85108972351&origin=inward

LSTM-PHV: prediction of human-virus protein-protein interactions by LSTM with word2vec Reviewed International journal

Tsukiyama S., Hasan M.M., Fujii S., Kurata H.

Briefings in bioinformatics 22 ( 6 ) 2021.11

Critical evaluation of web-based DNA N6-methyladenine site prediction tools Invited Reviewed

Hasan M.M., Shoombuatong W., Kurata H., Manavalan B.

Briefings in Functional Genomics 20 ( 4 ) 258 - 272 2021.07

Meta-i6mA: an interspecies predictor for identifying DNA N6-methyladenine sites of plant genomes by exploiting informative features in an integrative machine-learning framework Reviewed

Hasan M.M., Basith S., Khatun M.S., Lee G., Manavalan B., Kurata H.

Briefings in bioinformatics 22 ( 3 ) 2021.05

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Papers - KURATA Hiroyuki