Author

C.-J. Huang

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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Cell Biology· 2 Plant Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 3 Research Focus:clustering· 2 Approach:deep learning· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Assay:fluorescence / live-cell microscopy· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

C. Lei· 2 shared C.-W. Sun· 2 shared K. Goda· 2 shared A. Yasumoto· 1 shared C.-C. Chang· 1 shared H. Kobayashi· 1 shared H. Kurokawa· 1 shared H. Matsumura· 1 shared

Activity by Year

2019-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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2 shared papersCell Biology

C. Lei

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Temporal Transcriptome Analysis Uncovers Regulatory Modules Programming Embryo Development from Embryonic Morphogenesis to Post-Germination

Y.-C. Wang, W.-H. Hsieh, C.-J. Huang, Y.-T. Jhan, J. Zhan, C.-C. Chang, T.-F. Hsieh, J.-Y. Lin

The paper profiles soybean seed embryo transcriptomes across development to identify gene functions and regulatory networks involved in reserve accumulation and germination.

bioRxiv · Dec 26, 2023 · PreprintPlant Biology

Virtual-freezing fluorescence imaging flow cytometry

H. Mikami, M. Kawaguchi, C.-J. Huang, H. Matsumura, T. Sugimura, K. Huang, C. Lei, S. Ueno, T. Miura, T. Ito, K. Nagasawa, T. Maeno, H. Watarai, M. Yamagishi, S. Uemura, , K. Goda

This paper introduces a virtual-freezing fluorescence imaging flow cytometry method that enhances throughput without sacrificing sensitivity and spatial resolution, enabling high-dimensional analysis and classification.

bioRxiv · Jan 23, 2020Cell Biology

Classification of platelet aggregates by agonist type

Y. Zhou, A. Yasumoto, C. Lei, C.-J. Huang, H. Kobayashi, Y. Wu, S. Yan, C.-W. Sun, Y. Yatomi, K. Goda

This paper introduces a method to classify platelet aggregates by agonist type using convolutional neural networks trained on high-throughput imaging data.

bioRxiv · Oct 20, 2019 · PreprintCell Biology