Author

Shang Liu

BGI Group (China)ORCID 0000-0001-8050-7968Unclaimed 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:differential expression· 2 Assay:scRNA-seq· 2 Approach:statistical model· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Organism:human· 1

Frequent Co-Authors

Liang Li WuiD· 2 shared Shiping LiuiD· 2 shared Ao CheniD· 1 shared Biaofeng ZhouiD· 1 shared Bosiljka TasiciD· 1 shared Carl WardiD· 1 shared Chenyao Lou· 1 shared Chi Wai Wong· 1 shared

Activity by Year

2018-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersBioinformatics

Liang Li WuORCID

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3 papers

Recent·Most cited

BioinformaticsScientific ReportsPeer-reviewedMar 5, 2021

Single-cell differential splicing analysis reveals high heterogeneity of liver tumor-infiltrating T cells

Shang Liu, Biaofeng Zhou, Liang Li Wu…·Shiping Liu

The paper introduces DESJ-detection, a tool for analyzing differential splicing in single-cell RNA sequencing data, and identifies splicing differences in liver tumor-infiltrating T cells.

35 citations

humanscRNA-seq

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NeurosciencebioRxivPreprintMay 14, 2020

Spatially resolved gene regulatory and disease vulnerability map of the adult Macaque cortex

Ying Lei, Mengnan Cheng, Zihao Li…·Xun Xu

The paper integrates single-cell ATAC and transcriptomic data with spatial transcriptomics to map regulatory features and disease vulnerabilities in macaque cortex.

3 citations

non-human primatescATAC-seq

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BioinformaticsIEEE International Conference on Bioinformatics and Biomedicine (BIBM)Peer-reviewedDec 1, 2018

Learning Protein Structural Fingerprints under the Label-Free Supervision of Domain Knowledge

Yaosen Min, Shang Liu, Chenyao Lou, Xuefeng Cui

The paper introduces ContactLib-DNN, a method for detecting homologous proteins using structure fingerprints, achieving higher accuracy and speed than existing methods.

6 citations

protein structuredeep learning

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