Author

Hiroki Konishi

Institute of Medical Science, 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· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Organism:human· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Daisuke KomuraiD· 2 shared Hirotomo Koda· 2 shared Akihiro Kawabe· 1 shared Asami Yamamoto· 1 shared Hiroto KatohiD· 1 shared Keisuke Fukuta· 1 shared Ken Tominaga· 1 shared Masashi FukayamaiD· 1 shared

Activity by Year

2018-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Daisuke KomuraORCID

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2 papers

Recent·Most cited

Capturing the Differences between Humoral Immunity in the Normal and Tumor Environments from Repertoire-Seq of B-Cell Receptors Using Supervised Machine Learning

Hiroki Konishi, Daisuke Komura, Hiroto Katoh, Shinichiro Atsumi, Hirotomo Koda, Asami Yamamoto, Yasuyuki Seto, Masashi Fukayama, Rui Yamaguchi, Seiya Imoto, Shumpei Ishikawa

This study uses supervised machine learning to classify BCR/Ig sequences from normal and tumor tissues, finding distinct sequence-level features between the two environments.

BMC Bioinformatics · May 28, 2019Peer-reviewedBioinformatics

Luigi: Large-scale histopathological image retrieval system using deep texture representations

Daisuke Komura, Keisuke Fukuta, Ken Tominaga, Akihiro Kawabe, Hirotomo Koda, Ryohei Suzuki, Hiroki Konishi, Toshikazu Umezaki, Tatsuya Harada, Shumpei Ishikawa

Luigi retrieves histopathological images using deep texture features and a pre-trained CNN, enabling fast and accurate similarity searches across cancer cases.

bioRxiv · Jul 19, 2018 · PreprintPathology