Author

Takumi Ito

Institute of Science TokyoORCID 0000-0002-7380-2587Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Cancer Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Research Focus:benchmarking· 2 Research Focus:drug response / drug discovery· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:proteomics· 2 Assay:bulk RNA-seq· 1 Research Focus:database / resource· 1

Frequent Co-Authors

Hideyuki ShimizuiD· 4 shared Kohei Ota· 2 shared Akinori Kawaharada· 1 shared Hidekazu HishinumaiD· 1 shared Saori UematsuiD· 1 shared Satoshi OhnoiD· 1 shared Shinya KurodaiD· 1 shared Yiran WangiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 14 mutual co-author links

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4 shared papersBioinformatics

Hideyuki ShimizuORCID

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4 papers

Recent·Most cited

Dose-dependent modeling of combinatorial drug responses stratifies patient survival and reveals therapeutic vulnerabilities in precision oncology

Kohei Ota, Takumi Ito, Hideyuki Shimizu

XACT models dose-dependent drug responses using transcriptomic data to predict patient survival and identify therapeutic vulnerabilities in cancer.

bioRxiv · Apr 21, 2026 · PreprintCancer Biology

Pan-Pharmacological Drug-Target Interaction Prediction with 3D-Informed Protein Encoding at Scale

Akinori Kawaharada, Takumi Ito, Hideyuki Shimizu

OmniBind predicts drug-target interactions across multiple endpoints using structural protein encoding, outperforming existing methods and recovering known drug targets.

bioRxiv · Mar 30, 2026 · PreprintBioinformatics

A Functional Atlas of the Tardigrade Resistome Reveals a Diverse Molecular Toolkit for Extremotolerance

Takumi Ito, Kohei Ota, Hidekazu Hishinuma, Hideyuki Shimizu

The paper develops AEGIS, an AI tool, to discover novel guardian proteins in tardigrades, revealing a diverse molecular toolkit for extremotolerance.

bioRxiv · Jun 10, 2025 · PreprintBioinformatics

MetDeeCINE: Deciphering Metabolic Regulation through Deep Learning and Multi-Omics

Takumi Ito, Satoshi Ohno, Yiran Wang, Saori Uematsu, Shinya Kuroda, Hideyuki Shimizu

MetDeeCINE predicts enzyme-metabolite relationships using multi-omics data and outperforms existing models in predicting concentration control coefficients in E. coli.

bioRxiv · Mar 24, 2025 · PreprintSystems Biology