Author

Yuki Otani

Institute of Science TokyoORCID 0000-0002-7184-8776Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Yuki Otani1 paper · Hokkaido University

Biology Disciplines

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Systems Biology· 2 Bioinformatics· 1 Pharmacology & Toxicology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 4 Organism:human· 3 Research Focus:drug response / drug discovery· 2 Assay:proteomics· 2 Organism:bacteria (other)· 1 Research Focus:gene regulatory network inference· 1 Assay:metabolomics· 1 Assay:protein sequence· 1

Frequent Co-Authors

Hideyuki ShimizuiD· 4 shared Daisuke Koga· 2 shared Tomoya Sakuma· 2 shared Yasunari Wakizaka· 2 shared

Activity by Year

2025-2026 · peak 3/yr

Co-Author Collaboration Network

4 top collaborators · 4 mutual co-author links

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4 shared papersSystems Biology

Hideyuki ShimizuORCID

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4 papers

Recent·Most cited

Systems BiologybioRxivPreprintFeb 23, 2026

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.

DOI 10.64898/2026.02.21.707232

humanproteomics

BioinformaticsbioRxivPreprintApr 9, 2025

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.

DOI 10.1101/2025.04.09.647947

humanprotein structure

Systems BiologybioRxivPreprintFeb 27, 2025

AMP-Atlas: Comprehensive Atlas of Antimicrobial Peptides to Combat Multidrug-resistant Bacteria

Yuki Otani, Daisuke Koga, Yasunari Wakizaka, Hideyuki Shimizu

AMP-Atlas uses AI to predict antimicrobial peptides from sequences, outperforming existing methods and identifying new candidates in human bacteria.

DOI 10.1101/2025.02.27.640545

humanproteomics

Pharmacology & ToxicologybioRxivPreprintFeb 26, 2025

BaCNet: Deep Learning Accelerates Novel Antibiotic Discovery Against Resistant Pathogens

Yuki Otani, Daisuke Koga, Yasunari Wakizaka, Hideyuki Shimizu

BaCNet uses deep learning to predict antibiotic-target interactions and discover novel compounds against drug-resistant pathogens.

1 citation

bacteria (other)deep learning