Author

Hiroyoshi Nishikawa

National Cancer Centre JapanORCID 0000-0001-6563-9807Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Cancer Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:bulk RNA-seq· 2 Research Focus:clustering· 2 Assay:multiome· 2 Approach:statistical model· 2 Research Focus:batch correction / integration· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1

Frequent Co-Authors

Ko AbeiD· 2 shared Kodai MinouraiD· 2 shared Teppei ShimamuraiD· 2 shared Adam Stuart KibeliD· 1 shared Akihide YoshimiiD· 1 shared Asuka KawachiiD· 1 shared Atsuya Ishida· 1 shared Filipe L. De Carvalho· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Ko AbeORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

NECTIN4 defines a subtype-dependent tumor state coupling enfortumab vedotin targetability to antitumor immunity in bladder cancer

Kazuki Nishimura, Ryoichi Maenosono, Takuya Owari, Moritoshi Sakamoto, T Nishino, Kensuke Hirosuna, Takuya Tsujino, Takuya Izumi, Yuki Yoshikawa, Miyu Azuma, Sakura Inoue, Asuka Kawachi, Atsuya Ishida, Kei Nishimori, Junko Mukohyama, , Akihide Yoshimi

The paper identifies NECTIN4 as a subtype-dependent determinant of tumor state and antitumor immunity in bladder cancer, linking its expression to response to enfortumab vedotin and checkpoint inhibitors.

bioRxiv · Oct 9, 2026 · PreprintCancer Biology

scMM: Mixture-of-experts multimodal deep generative model for single-cell multiomics data analysis.

Kodai Minoura, Ko Abe, Hyunha Nam, Hiroyoshi Nishikawa, Teppei Shimamura

scMM is a deep generative model that extracts interpretable joint representations and enables cross-modal generation for single-cell multiomics data, improving clustering and data integration.

Cell Reports Methods · Sep 1, 2021Peer-reviewedBioinformatics

Model-based cell clustering and population tracking for time-series flow cytometry data

Kodai Minoura, Ko Abe, Yuka Maeda, Hiroyoshi Nishikawa, Teppei Shimamura

CYBERTRACK is a statistical framework for clustering and tracking cell populations in time-series flow cytometry data, showing consistent results with known lymphocyte dynamics.

BMC Bioinformatics · Dec 1, 2019Peer-reviewedSystems Biology