Author

Chunlei Liu

The University of SydneyORCID 0000-0002-1138-7488Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Approach:deep learning· 3 Assay:multiome· 3 Assay:scRNA-seq· 3 Research Focus:cell type annotation· 2 Research Focus:clustering· 2 Assay:scATAC-seq· 2 Approach:benchmark study· 1

Frequent Co-Authors

Pengyi YangiD· 5 shared Benjamin L. ParkeriD· 1 shared Di XiaoiD· 1 shared Di XiaoiD· 1 shared Ellis PatrickiD· 1 shared Hani Jieun KimiD· 1 shared Hao Ming HuangiD· 1 shared James G. BurchfieldiD· 1 shared

Activity by Year

2023-2026 · peak 3/yr

Co-Author Collaboration Network

10 top collaborators · 14 mutual co-author links

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5 shared papersBioinformatics

Pengyi YangORCID

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Author profile →

5 papers

Recent·Most cited

Interpretable deep generative ensemble learning for single-cell omics with Hydra

Manoj M Wagle, Chunlei Liu, Zunpeng Liu, Yongheng Wang, M Kellis, Ellis Patrick, Pengyi Yang

Hydra is a deep generative framework for single-cell omics data that improves cell type annotation and feature selection, particularly for rare populations.

Molecular Systems Biology · Apr 11, 2026Peer-reviewedBioinformatics

Multi-task benchmarking of single-cell multimodal omics integration methods

Chunlei Liu, Sichang Ding, Hani Jieun Kim, Siqu Long, Di Xiao, Shila Ghazanfar, Pengyi Yang

This study benchmarks single-cell multimodal omics integration methods across multiple tasks to guide method selection based on data characteristics and study goals.

bioRxiv · Sep 15, 2024 · PreprintBioinformatics

SnapKin: a snapshot deep learning ensemble for kinase-substrate prediction from phosphoproteomics data

Di Xiao, Michael Lin, Chunlei Liu, Thomas A. Geddes, James G. Burchfield, Benjamin L. Parker, Sean J. Humphrey, Pengyi Yang

The paper develops SnapKin, a deep learning ensemble model for kinase-substrate prediction from phosphoproteomics data, achieving the best performance in this task.

NAR Genomics and Bioinformatics · Oct 11, 2023Peer-reviewedBioinformatics

Ensemble deep learning of embeddings for clustering multimodal single-cell omics data

Lijia Yu, Chunlei Liu, Jean Yee Hwa Yang, Pengyi Yang

SnapCCESS improves clustering of multimodal single-cell omics data by integrating multiple data modalities using an ensemble deep learning framework.

Bioinformatics · Jun 1, 2023Peer-reviewedBioinformatics

Multi-task learning from single-cell multimodal omics with Matilda

Chunlei Liu, Hao Ming Huang, Pengyi Yang

Matilda is a multi-task learning method that integrates single-cell multimodal omics data by performing data simulation, dimension reduction, cell type classification, and feature selection.

Nucleic Acids Research · Feb 24, 2023Peer-reviewedBioinformatics