Author

Yumeng Wei

Chinese University of Hong Kong

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· 3

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Assay:scRNA-seq· 2 Research Focus:batch correction / integration· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:contrastive learning· 1 Research Focus:deconvolution· 1

Frequent Co-Authors

Yixuan WangiD· 3 shared Yu LiiD· 3 shared Jiuming WangiD· 2 shared Yunxiang LiiD· 2 shared Jianzhu MaiD· 1 shared Jiayang CheniD· 1 shared Juan ShuiD· 1 shared Liang HongiD· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersBioinformatics

Yixuan WangORCID

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Author profile →

3 papers

Recent·Most cited

AcrNET: Predicting Anti-CRISPR with Deep Learning

Yunxiang Li, Yumeng Wei, Sheng Yong Xu, Qingxiong Tan, Licheng Zong, Jiuming Wang, Yixuan Wang, Jiayang Chen, Liang Hong, Yu Li

AcrNET predicts anti-CRISPR proteins using a deep learning model, achieving significant performance improvements and class-level predictions.

Bioinformatics · Apr 21, 2023Peer-reviewedBioinformatics

Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

Yanshuo Chen, Yixuan Wang, Yuelong Chen, Yuqi Cheng, Yumeng Wei, Yunxiang Li, Jiuming Wang, Yingying Wei, Ting‐Fung Chan, Yu Li

TAPE deconvolves bulk RNA-seq data into cell-type fractions and gene expression using a deep autoencoder, showing better performance and robustness compared to existing methods.

Nature Communications · Nov 8, 2022Peer-reviewedBioinformatics

Contrastive Cycle Adversarial Autoencoders for Single-cell Multi-omics Alignment and Integration

Xuesong Wang, Zhihang Hu, Tingyang Yu, Yixuan Wang, Ruijie Wang, Yumeng Wei, Juan Shu, Jianzhu Ma, Yu Li

This paper introduces a framework to align and integrate single-cell RNA-seq and ATAC-seq data, achieving higher accuracy than existing methods on both simulated and real data.

bioRxiv · Dec 12, 2021 · PreprintBioinformatics