Author

Taku Monjo

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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Genomics· 1

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Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Organism:human· 1 Assay:imaging· 1 Assay:spatial transcriptomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Masaru KoidoiD· 1 shared Satoi NagasawaiD· 1 shared Yoichiro KamataniiD· 1 shared Yutaka SuzukiiD· 1 shared

Activity by Year

2022-2022 · peak 1/yr

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4 top collaborators · 6 mutual co-author links

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1 shared paperGenomics

Masaru KoidoORCID

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1 paper

Recent·Most cited

Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

Taku Monjo, Masaru Koido, Satoi Nagasawa, Yutaka Suzuki, Yoichiro Kamatani

The paper develops DeepSpaCE, a deep learning model that predicts spatial transcriptomics profiles to improve histological interpretation of breast cancer tissue sections without additional experiments.

Scientific Reports · Mar 8, 2022Peer-reviewedGenomics