Author

Xin Yang

Georgia Institute of Technology

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Biology Disciplines

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Bioinformatics· 2 Molecular Biology· 1

Signature Assays, Organisms & Methods

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Assay:protein sequence· 2 Approach:deep learning· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Organism:mouse· 1 Approach:transformer / foundation model· 1

Frequent Co-Authors

Hanli JiangiD· 2 shared Jianyu ReniD· 2 shared Li LiniD· 2 shared Andrew S. LemoffiD· 1 shared Bin TaniD· 1 shared Chao XingiD· 1 shared Dongliang Deng· 1 shared Hai TongiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 20 mutual co-author links

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2 shared papersBioinformatics

Hanli JiangORCID

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3 papers

Recent·Most cited

ETAP-CLF: an ESM3-based transformer attention framework for binary protein classification

Jianyu Ren, Hanli Jiang, Puchangxin Li, Xin Yang, Lingwei Mei, Hai Tong, Li Lin

ETAP-CLF uses ESM3 embeddings and transformer attention to classify proteins into binary categories, achieving high AUROC scores across different biological processes.

bioRxiv · Aug 20, 2026 · PreprintBioinformatics

EPOP Restricts PRC2.1 Targeting to Chromatin by Directly Modulating Enzyme Complex Dimerization

Lihu Gong, Xiuli Liu, Xin Yang, Ze Yu, Mohd Younus Bhat, Siming Chen, Andrew S. Lemoff, Chao Xing, Xin Liu

The paper shows EPOP inhibits PRC2.1 by disrupting its dimerization, reducing chromatin association, and preventing over-repression of gene regulators during early differentiation.

Nature Communications · Jan 10, 2026Peer-reviewedMolecular Biology

SenSeqNet: A Deep Learning Framework for Cellular Senescence Detection from Protein Sequences

Hanli Jiang, Dongliang Deng, Yu Lai Yuan, Jianyu Ren, Xin Yang, Siyi Liu, Bin Tan, Li Lin, Lubin LIU

SenSeqNet detects cellular senescence from protein sequences with 83.55% accuracy using a hybrid LSTM-CNN architecture and ESM-2 embeddings.

Aging Cell · Dec 23, 2025Peer-reviewedBioinformatics