Author

Zenki Ikeda

Kindai University

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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Developmental Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:imaging· 2 Organism:mouse· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:differential expression· 1 Research Focus:dimensionality reduction· 1 Approach:experimental assay· 1

Frequent Co-Authors

Akira FunahashiiD· 2 shared Daisuke MashikoiD· 2 shared Kazuo YamagataiD· 2 shared Takahiro YamadaiD· 2 shared Yuta TokuokaiD· 2 shared Noriko F. HiroiiD· 1 shared Tetsuya J. KobayashiiD· 1 shared Tetsuya KobayashiiD· 1 shared

Activity by Year

2020-2021 · peak 1/yr

Co-Author Collaboration Network

8 top collaborators · 26 mutual co-author links

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2 shared papersDevelopmental Biology

Akira FunahashiORCID

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2 papers

Recent·Most cited

Deep learning-based algorithm for predicting the live birth potential of mouse embryos

Yuta Tokuoka, Takahiro Yamada, Daisuke Mashiko, Zenki Ikeda, Tetsuya J. Kobayashi, Kazuo Yamagata, Akira Funahashi

The paper develops a deep learning algorithm to predict mouse embryo live birth potential from nuclear structural features in fluorescence images, achieving 83.87% accuracy.

bioRxiv · Aug 19, 2021 · PreprintDevelopmental Biology

Convolutional Neural Network-Based Instance Segmentation Algorithm to Acquire Quantitative Criteria of Early Mouse Development

Yuta Tokuoka, Takahiro Yamada, Daisuke Mashiko, Zenki Ikeda, Noriko F. Hiroi, Tetsuya Kobayashi, Kazuo Yamagata, Akira Funahashi

This paper introduces a deep learning-based instance segmentation algorithm, QCA Net, which outperforms existing methods in segmenting nuclei in mouse embryos and provides quantitative developmental metrics.

npj Systems Biology and Applications · Oct 20, 2020Peer-reviewedSystems Biology