Author

Shengkun Ni

Chinese Academy of Sciences

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:drug response / drug discovery· 3 Assay:bulk RNA-seq· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:diffusion model· 1 Assay:protein structure· 1 Approach:statistical model· 1

Frequent Co-Authors

Mingyue ZhengiD· 3 shared Xiangtai KongiD· 3 shared Ning Qu· 2 shared Runze ZhangiD· 2 shared Sulin ZhangiD· 2 shared Xiaochu Tong· 2 shared Xutong LiiD· 2 shared Yitian WangiD· 2 shared

Activity by Year

2023-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 41 mutual co-author links

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3 shared papersBioinformatics

Mingyue ZhengORCID

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Author profile →

3 papers

Recent·Most cited

Identify compound-protein interaction with knowledge graph embedding of perturbation transcriptomics

Shengkun Ni, Xiangtai Kong, Yingying Zhang, Zhengyang Chen, Zhaokun Wang, Zunyun Fu, Ruifeng Huo, Xiaochu Tong, Ning Qu, Xiaolong Wu, Kun Wang, Wei Dong Zhang, Runze Zhang, Zimei Zhang, Jiangshan Shi, , Mingyue Zheng

PertKGE improves compound-protein interaction prediction using knowledge graph embedding of perturbation transcriptomics, outperforming baselines in cold-start settings and identifying novel therapeutic targets.

bioRxiv · Apr 8, 2024 · PreprintBioinformatics

PackDock: a Diffusion Based Side Chain Packing Model for Flexible Protein-Ligand Docking

Runze Zhang, Xinyu Jiang, Duanhua Cao, Jie Yu, Mingan Chen, Zhehuan Fan, Xiangtai Kong, Jiacheng Xiong, Zimei Zhang, Wei Zhang, Shengkun Ni, Yitian Wang, Shenghua Gao, Mingyue Zheng

PackDock predicts accurate protein-ligand complex conformations using a diffusion model for side-chain packing, validated through multiple docking experiments.

bioRxiv · Jan 31, 2024 · PreprintBioinformatics

TranSiGen: Deep representation learning of chemical-induced transcriptional profile

Xiaochu Tong, Ning Qu, Xiangtai Kong, Shengkun Ni, Kun Wang, Lehan Zhang, Yiming Wen, Sulin Zhang, Xutong Li, Mingyue Zheng

TranSiGen denoises and reconstructs chemical-induced transcriptional profiles using deep generative modeling, effectively predicting differential expression genes and serving as alternative phenotype information for drug discovery.

bioRxiv · Nov 12, 2023 · PreprintBioinformatics