Author

Tomoya Sakuma

Institute of Science Tokyo

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Systems Biology· 2 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:deep learning· 2 Research Focus:gene regulatory network inference· 2 Assay:bulk RNA-seq· 1 Research Focus:drug response / drug discovery· 1 Assay:metabolomics· 1 Assay:protein sequence· 1 Assay:protein structure· 1

Frequent Co-Authors

Hideyuki ShimizuiD· 3 shared Yuki OtaniiD· 2 shared Satoshi OhnoiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

3 top collaborators · 2 mutual co-author links

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3 shared papersSystems Biology

Hideyuki ShimizuORCID

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3 papers

Recent·Most cited

Cell-type-resolved Metabolic Flux Inference Reveals Stromal Metabolic Reprogramming Across Human Cardiomyopathies

Tomoya Sakuma, Satoshi Ohno, Hideyuki Shimizu

The paper develops a cell-type-resolved metabolic flux inference pipeline to analyze human cardiomyopathies, revealing stromal metabolic reprogramming and genotype-specific metabolic alterations.

bioRxiv · Apr 21, 2026 · PreprintSystems Biology

Geometric-Evolutionary Deep Learning Decodes the Human GPCR-Metabolome Interactome and Enables Systematic De-Orphanization

Tomoya Sakuma, Yuki Otani, Hideyuki Shimizu

G-LEAP uses deep learning to predict GPCR-ligand interactions, identifying endogenous ligands and orphan ligands with high accuracy.

bioRxiv · Feb 23, 2026 · PreprintSystems Biology

High-Resolution Mapping of the Human E3-Substrate Interactome using Ubicon Uncovers Network Architecture and Cancer Vulnerabilities

Tomoya Sakuma, Yuki Otani, Hideyuki Shimizu

The paper introduces Ubicon, a deep learning framework for predicting human E3-substrate interactions, revealing network architecture and cancer-related vulnerabilities.

bioRxiv · Apr 9, 2025 · PreprintBioinformatics