Author

Shion Hosoda

Hitachi (Japan)ORCID 0000-0001-5093-6833Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Assay:DNA methylation· 1 Research Focus:drug response / drug discovery· 1 Organism:E. coli· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Michiaki HamadaiD· 2 shared Miwa Sato· 2 shared Atsushi MochizukiiD· 1 shared Hisashi IwataiD· 1 shared Hitoshi IuchiiD· 1 shared Kanami Yagimoto· 1 shared Keisuke YamadaiD· 1 shared Maiko Tanabe· 1 shared

Activity by Year

2021-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 19 mutual co-author links

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2 shared papersBioinformatics

Michiaki HamadaORCID

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3 papers

Recent·Most cited

BioinformaticsBMC BioinformaticsPeer-reviewedSep 10, 2024

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks

Shion Hosoda, Hisashi Iwata, Takuya Miura…·Miwa Sato

BayesianSSA integrates environmental information from perturbation data into structural sensitivity analysis to predict enzyme perturbation responses in metabolic networks.

DOI 10.1186/s12859-024-05921-4

E. colimetabolomics

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BioinformaticsBioinformaticsPeer-reviewedSep 7, 2024

Prediction of antibiotic resistance mechanisms using a protein language model

Kanami Yagimoto, Shion Hosoda, Miwa Sato, Michiaki Hamada

This paper introduces a protein language model to predict antibiotic resistance mechanisms in ARGs, showing improved performance and interpretability on diverse datasets.

25 citations

protein sequencedeep learning

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BioinformaticsComputational and Structural Biotechnology JournalPeer-reviewedJan 1, 2021

Representation learning applications in biological sequence analysis

Hitoshi Iuchi, Taro Matsutani, Keisuke Yamada…·Michiaki Hamada

This paper reviews the application of representation learning in biological sequence analysis, focusing on how embedding techniques transform sequences for functional and structural insights.

83 citations

DNA methylationproteomics

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