Author

Yang Yang

The University of QueenslandORCID 0000-0002-4840-3023Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Yang Yang31 papers · MRC Laboratory of Molecular Biology

Biology Disciplines

Click to filter author papers

Bioinformatics· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:statistical model· 2 Research Focus:batch correction / integration· 1 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Assay:CITE-seq· 1 Research Focus:clustering· 1 Approach:deep learning· 1

Frequent Co-Authors

Di YuiD· 2 shared Adelle C.F. CosteriD· 1 shared Di WuiD· 1 shared Guangxin Su· 1 shared Hanchen Wang· 1 shared Hongjian SuniD· 1 shared Jialei GongiD· 1 shared Marc Ronald WilkinsiD· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

2 shared papersBioinformatics

Di YuORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

TCRseek: Scalable Approximate Nearest Neighbor Search for T-Cell Receptor Repertoires via Windowed k-mer Embeddings

TCRseek efficiently searches TCR repertoires using approximate nearest neighbor search with windowed k-mer embeddings, achieving high accuracy and speed.

bioRxiv · Mar 24, 2026 · PreprintBioinformatics

Inferring gene regulatory networks by hypergraph variational autoencoder

Guangxin Su, Hanchen Wang, Ying Zhang, Adelle C.F. Coster, Marc Ronald Wilkins, Pablo F. Cañete, Di Yu, Yang Yang, Wenjie Zhang

This paper introduces HyperG-VAE, a Bayesian deep generative model for GRN inference using scRNA-seq data, showing effectiveness in clustering, visualization, and revealing gene regulation patterns.

bioRxiv · Apr 1, 2024 · PreprintBioinformatics

Dimensionality reduction by UMAP reinforces sample heterogeneity analysis in bulk transcriptomic data

Yang Yang, Hongjian Sun, Yu Zhang, Tiefu Zhang, Jialei Gong, Yunbo Wei, Yong‐Gang Duan, Minglei Shu, Yuchen Yang, Di Wu, Di Yu

This study compares dimensionality reduction methods for bulk transcriptomic data, finding UMAP better preserves sample neighbourhood and clustering accuracy than PCA, MDS, and t-SNE.

Cell Reports · Jul 1, 2021Peer-reviewedBioinformatics