Author

Die Qiu

ShanghaiTech University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 1 Cancer Biology· 1 Genomics· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Organism:human· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

Kaixuan DiaoiD· 3 shared Weiliang Wang· 2 shared Xiangyu Zhao· 2 shared Xiaopeng XiongiD· 2 shared Xinxiang LiiD· 2 shared Xuesong LiuiD· 2 shared Chenxu WuiD· 1 shared Dongliang XuiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 34 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

3 shared papersBioinformatics

Kaixuan DiaoORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

TcrDesign: De novo design of epitope specific full-length T cell receptors

Kaixuan Diao, Jing Chen, Xiangyu Zhao, Tao Wu, Die Qiu, W Wang, Haopeng Wang, Xue-Song Liu

TcrDesign generates experimentally validated, functional, full-length TCRs using deep learning, achieving state-of-the-art performance in binding prediction and sequence generation.

Science China Life Sciences · Apr 28, 2026Peer-reviewedBioinformatics

Single-cell copy number alteration signature analysis reveals masked patterns and potential biomarkers for cancer

Chenxu Wu, Ziyu Tao, Wen Chen, Nan Wang, jinyu Wang, jiayu Shen, Tao Wu, Die Qiu, Kaixuan Diao, Xiangyu Zhao, Tianzhu Lu, Lin Zhang, Weiliang Wang, Xinxing Li, Xinxiang Li, , Xuesong Liu

This study analyzes CNA signatures in single-cell and bulk samples of hepatocellular carcinoma, revealing masked patterns and potential biomarkers for prognosis and treatment.

Communications Biology · Nov 17, 2025Peer-reviewedGenomics

Precise metabolic dependencies of cancer through deep learning and validations

Tao Wu, Xiangyu Zhao, Yu Zhang, Die Qiu, Kaixuan Diao, Dongliang Xu, Weiliang Wang, Xiaopeng Xiong, Xinxiang Li, Xuesong Liu

This study uses deep learning to predict metabolic vulnerabilities in cancer cells and validates findings using TCGA data, showing improved clinical responses to targeted therapies.

Cell Reports · Jul 1, 2025Peer-reviewedCancer Biology