Author

Jiuxin Feng

Shandong 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:batch correction / integration· 1 Research Focus:benchmarking· 1 Approach:contrastive learning· 1 Assay:imaging· 1 Assay:protein sequence· 1 Assay:proteomics· 1 Research Focus:sequence-to-function modeling· 1

Frequent Co-Authors

Junru JiniD· 2 shared Leyi WeiiD· 2 shared Quan ZouiD· 2 shared Ran SuiD· 2 shared Ruheng WangiD· 2 shared Yi JiangiD· 2 shared Anjun MaiD· 1 shared Chaoyang YaniD· 1 shared

Activity by Year

2022-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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2 shared papersBioinformatics

Junru JinORCID

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Author profile →

3 papers

Recent·Most cited

Triple-effect correction for Cell Painting data with contrastive and domain-adversarial learning

Chengwei Yan, Yu Zhang, Jiuxin Feng, Heyang Hua, Zhihan Ruan, Zhen Li, Siyu Li, Chaoyang Yan, Pingjing Li, Jian Liu, Shengquan Chen

The paper introduces cpDistiller, a method to correct three technical effects in Cell Painting data using contrastive and domain-adversarial learning, improving analysis reliability and phenotypic response detection.

Nature Communications · Jul 25, 2025Peer-reviewedBioinformatics

DeepBIO is an automated and interpretable deep-learning platform for biological sequence prediction, functional annotation, and visualization analysis

Ruheng Wang, Yi Jiang, Junru Jin, Chenglin Yin, Haoqing Yu, Fengsheng Wang, Jiuxin Feng, Ran Su, Kenta Nakai, Quan Zou, Leyi Wei

DeepBIO is a deep-learning platform that automates biological sequence analysis, offering 42 algorithms and visualization tools for functional prediction and annotation.

Nucleic Acids Research · Jan 19, 2023Peer-reviewedBioinformatics

Explainable deep graph learning accurately modeling the peptide secondary structure prediction

Yi Jiang, Ruheng Wang, Jiuxin Feng, Junru Jin, Sirui Liang, Zhongshen Li, Yingying Yu, Anjun Ma, Ran Su, Quan Zou, Qin Ma, Leyi Wei

This paper introduces PHAT, a deep graph learning framework for peptide secondary structure prediction, achieving better accuracy and interpretability for short peptides.

bioRxiv · Jun 9, 2022 · PreprintBioinformatics