Author

Liangjie Liu

Shanghai Jiao Tong 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· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:protein sequence· 2 Approach:deep learning· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1 Organism:mouse· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

Guang HeiD· 3 shared Mofan Feng· 2 shared Qing LüiD· 2 shared Xiaoxi WeiiD· 2 shared Decheng ReniD· 1 shared Fengping Yang· 1 shared Hua WangiD· 1 shared Ke YangiD· 1 shared

Activity by Year

2024-2025 · peak 2/yr

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10 top collaborators · 25 mutual co-author links

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3 shared papersBioinformatics

Guang HeORCID

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3 papers

Recent·Most cited

NUSAP1 regulates mitotic processes via KIF2C interaction and AURKA phosphorylation in primary microcephaly

Decheng Ren, Keyi Li, Zhen Liu, Yan Bi, Liangjie Liu, Lei Ji, Ke Yang, Yingying Luo, Le Luo, Yan Yang, Yang Li, Fengping Yang, Hua Wang, Guang He, Xiao Mao

This study identifies NUSAP1 mutations in primary microcephaly, showing they disrupt mitotic processes through interactions with KIF2C and AURKA phosphorylation.

bioRxiv · May 16, 2025 · PreprintGenetics

PSTP: Decoding Latent Sequence Grammar for Protein Phase Separation through Transfer Learning and Attention

Mofan Feng, Liangjie Liu, Zhuo-Ning Xian, Xiaoxi Wei, Keyi Li, Wenqian Yan, Qing Lü, Yi Shi, Guang He

PSTP predicts protein phase separation regions using transfer learning and attention, achieving 84% accuracy on PhaSePro and improving correlation coefficients over existing models.

Briefings in Bioinformatics · Apr 28, 2025Peer-reviewedBioinformatics

Decoding Missense Variants by Incorporating Phase Separation via Machine Learning

Mofan Feng, Xiaoxi Wei, Xi Zheng, Liangjie Liu, Lin Lin, Manying Xia, Guang He, Yi Y. Shi, Qing Lü

PSMutPred predicts the impact of missense variants on phase separation using machine learning, improving the interpretation of variants of unknown significance in intrinsically disordered regions.

Nature Communications · Sep 27, 2024Peer-reviewedBioinformatics