Author

Zongyuan Ge

Monash UniversityORCID 0000-0002-5880-8673Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1 Research Focus:dimensionality reduction· 1 Assay:electronic health records· 1 Assay:fluorescence / live-cell microscopy· 1

Frequent Co-Authors

Aaron R. JexiD· 1 shared Anusuya WillisiD· 1 shared Aonan He· 1 shared Arash ZamyadiiD· 1 shared Chen Yang· 1 shared Cheryl BertelkampiD· 1 shared Chunfeng YangiD· 1 shared Douglas R. BrumleyiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperMicrobiology

Aaron R. JexORCID

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2 papers

Recent·Most cited

G3DCT: An Interpretable Spatial Grid-based Framework with Temporal Convolution-Transformer for EEG Artifact Identification

Aonan He, Xi Wang, Jiangwei Yu, Xiaojia Wang, Zongyuan Ge, Youyong Kong, Guanyu Yang, Chunfeng Yang, Chen Yang, Miao Cao

G3DCT is a deep learning framework for EEG artifact identification that outperforms existing models, achieving a 2.8% F1-score improvement on combined artifacts.

bioRxiv · Feb 2, 2026 · PreprintNeuroscience

Machine learning driven image segmentation and shape clustering of algal microscopic images obtained from various water types

Filippo Nelli, Zongyuan Ge, Linda L. Blackall, Negar Taheriashtiani, Rebekah Henry, Douglas R. Brumley, Michael Grace, Aaron R. Jex, Michael D. Burch, Tsair–Fuh Lin, Cheryl Bertelkamp, Anusuya Willis, Li Gao, Jonathan Schmidt, Nicholas D. Crosbie, Arash Zamyadi

This study applies FastSAM for automated algal cell segmentation and clustering in water samples, showing high accuracy for single cells and varying clustering performance.

Computational and Structural Biotechnology Reports · Aug 28, 2024Peer-reviewedMicrobiology