Author

Enyan Liu

Zhejiang 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

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:protein sequence· 2 Approach:statistical model· 2 Organism:bacteria (other)· 1 Research Focus:benchmarking· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

Cong FengiD· 2 shared Haoyu ChaoiD· 2 shared Liya LiuiD· 2 shared Luyao Xie· 2 shared Ming CheniD· 2 shared Sida LiiD· 2 shared Yanshi HuiD· 2 shared Yanyan Zhu· 2 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioinformatics

Cong FengORCID

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3 papers

Recent·Most cited

mirCCC: Repression-aware graph learning for miRNA-mediated cell-cell communication inference

Yuehua Chen, Jiaming Cui, Shilong Zhang, Enyan Liu, Luyao Xie, Cong Feng, Ming Chen

mirCCC infers microRNA-mediated cell-cell communication from single-cell RNA sequencing by detecting coordinated gene expression changes and models miRNA activity and transfer capacity.

bioRxiv · Jul 1, 2026 · PreprintBioinformatics

HPInet: Interpretable prediction of Host-Pathogen protein-protein Interactions using a transformer-based neural network

Yueming Hu, Liya Liu, Yanyan Zhu, Enyan Liu, Haoyu Chao, Sida Li, Cong Feng, Yanshi Hu, Yuhao Chen, Shilong Zhang, Yifan Chen, Luyao Xie, Yejun Wang, Ming Chen

HPInet predicts host-pathogen protein-protein interactions using a transformer-based model, achieving high accuracy and identifying key residues from sequence data.

bioRxiv · Aug 2, 2025 · PreprintBioinformatics

Improved Prediction of Bacterial Type VI Secretion Effector Proteins Using an Integrated Convolutional Neural Network Model Combining N-terminal Signal Sequences, Evolutionary Information and Pre-Trained Protein Language Features

Yueming Hu, Minqing Yan, Yanyan Zhu, Haoyu Chao, Sida Li, Qinyang Ni, Yanshi Hu, Enyan Liu, Liya Liu, Yifan Chen, Zeya Zhou, Yuhao Chen, Shilong Zhang, Yejun Wang, Cong Feng, Ming Chen

This paper introduces T6CNN, a model that improves T6SE prediction by integrating signal sequences, evolutionary data, and pre-trained features, achieving higher accuracy, sensitivity, and specificity than existing tools.

bioRxiv · Mar 7, 2025 · PreprintBioinformatics