Author

Xiaorong Li

Minzu University of ChinaORCID 0000-0003-0641-2797Unclaimed ORCID profile · follow to get notified of new preprints

Additional preprints under this name without ORCID

Xiaorong Li1 paper · Beijing Advanced Innovation Center for Food Nutrition and Human Health

Biology Disciplines

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Bioinformatics· 2 Immunology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:bulk RNA-seq· 1 Research Focus:drug response / drug discovery· 1 Assay:scRNA-seq· 1 Approach:statistical model· 1

Frequent Co-Authors

Jielong Zhou· 2 shared Mengchen PuiD· 2 shared Weisheng ZhengiD· 2 shared Yingsheng ZhangiD· 2 shared Bidesh MahataiD· 1 shared Dejia WeniD· 1 shared Gongxin Peng· 1 shared Kai TianiD· 1 shared

Activity by Year

2023-2024 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersBioinformatics

Jielong Zhou

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Author profile →

3 papers

Recent·Most cited

Multiomic Screening Unravels the Immunometabolic Signatures and Drug Targets of Age-Related Macular Degeneration

Xuehao Cui, Qiuchen Zhao, Bidesh Mahata, Dejia Wen, Patrick Yu‐Wai‐Man, Xiaorong Li

This study uses multiomic methods to identify immunometabolic pathways and drug targets in AMD, highlighting key genes and pathways involved in disease progression and potential therapeutic interventions.

bioRxiv · May 7, 2024 · PreprintImmunology

Sparse Modeling of Genomic Landscape Identifies Pathogenic Processes and Therapeutic Targets in Metastatic Breast Cancer

Mengchen Pu, Kai Tian, Weisheng Zheng, Xiaorong Li, Keyue Fan, Liang Zheng, Jielong Zhou, Yingsheng Zhang

This study uses a deep learning model to identify mutational signatures in metastatic breast cancer and link them to therapeutic targets.

bioRxiv · Aug 29, 2023 · PreprintBioinformatics

Using graph-based model to identify cell specific synthetic lethal effects

Mengchen Pu, Kaiyang Cheng, Xiaorong Li, Yucui Xin, Lanying Wei, Sutong Jin, Weisheng Zheng, Gongxin Peng, Qihong Tang, Jielong Zhou, Yingsheng Zhang

This paper introduces a deep learning model using graph-based representation to predict cell-specific synthetic lethal gene pairs, enhancing cancer precision medicine.

Computational and Structural Biotechnology Journal · Jan 1, 2023Peer-reviewedBioinformatics