Author

Thomas N. Sato

Advanced Telecommunications Research Institute InternationalORCID 0000-0003-1023-9958Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:deconvolution· 1 Research Focus:drug response / drug discovery· 1 Assay:GWAS summary statistics· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Kengo TejimaiD· 2 shared Satoshi KozawaiD· 2 shared Hirona Yokoyama· 1 shared Hotaka Doi· 1 shared Kyoji Urayama· 1 shared Shunki Takagi· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

6 top collaborators · 15 mutual co-author links

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2 shared papersBioinformatics

Kengo TejimaORCID

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2 papers

Recent·Most cited

BioinformaticsPLoS GeneticsPeer-reviewedNov 18, 2024

Cell type-specific weighting-factors to solve solid organs-specific limitations of single cell RNA-sequencing

Kengo Tejima, Satoshi Kozawa, Thomas N. Sato

The paper introduces cell type-specific weighting-factors to address limitations in scRNA-seq analysis of solid tissues, improving bulk RNA-seq reconstitution and deconvolution accuracy.

1 citation

humanscRNA-seq

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BioinformaticsBioinformatics AdvancesPeer-reviewedJan 1, 2023

Latent disease similarities and therapeutic repurposing possibilities uncovered by multi-modal generative topic modeling of human diseases

Satoshi Kozawa, Hirona Yokoyama, Kyoji Urayama…·Thomas N. Sato

This paper uses multi-modal generative topic modeling to identify latent disease similarities and therapeutic targets for repurposing across human diseases.

3 citations

humanGWAS summary statistics

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