Author

Iori Azuma

The University of 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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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 3 Research Focus:deconvolution· 3 Approach:statistical model· 3 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Organism:human· 1 Organism:mouse· 1 Organism:rat· 1

Frequent Co-Authors

Hiroyuki KusuharaiD· 3 shared Tadahaya MizunoiD· 3 shared Katsuhisa Morita· 2 shared Yutaka SuzukiiD· 2 shared

Activity by Year

2023-2024 · peak 2/yr

Co-Author Collaboration Network

4 top collaborators · 6 mutual co-author links

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3 shared papersBioinformatics

Hiroyuki KusuharaORCID

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3 papers

Recent·Most cited

GLDADec: marker-gene guided LDA modelling for bulk gene expression deconvolution

Iori Azuma, Tadahaya Mizuno, Hiroyuki Kusuhara

GLDADec deconvolves bulk gene expression data using marker gene names to estimate cell type proportions, outperforming existing methods and enabling clinical applications.

bioRxiv · Jan 8, 2024 · PreprintBioinformatics

Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases

Katsuhisa Morita, Tadahaya Mizuno, Iori Azuma, Yutaka Suzuki, Hiroyuki Kusuhara

This paper develops a rat-specific deconvolution method to retrieve immune cell trafficking information from toxicogenomics databases, finding species-specific differences and identifying compound clusters based on immune cell profiles.

Toxicological Sciences · Nov 5, 2023Peer-reviewedBioinformatics

Investigation of the usefulness of liver-specific deconvolution method toward legacy data utilization

Iori Azuma, Tadahaya Mizuno, Katsuhisa Morita, Yutaka Suzuki, Hiroyuki Kusuhara

The paper evaluates a liver-specific deconvolution method on legacy data, showing improved extrapolation when using optimized reference cell sets for liver transcriptome analysis.

bioRxiv · Apr 19, 2023 · PreprintBioinformatics