Author

Shuangxia Ren

University of PittsburghORCID 0000-0001-6591-0695Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Approach:deep learning· 2 Research Focus:drug response / drug discovery· 2 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

Xinghua LuiD· 3 shared Gregory F. CooperiD· 2 shared Anchen QiuiD· 1 shared Binfeng LuiD· 1 shared Boyang Sun· 1 shared Bryan Andrews· 1 shared Han ZhangiD· 1 shared Joseph Ramsey· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 37 mutual co-author links

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3 shared papersBioinformatics

Xinghua LuORCID

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3 papers

Recent·Most cited

Causal Modeling Reveals Cell-Cell Communication Dynamics in the Tumor Microenvironment During Anti-PD-1 Therapy in Breast Cancer Patients

Anchen Qiu, Han Zhang, Joseph Ramsey, Bryan Andrews, Boyang Sun, Shuangxia Ren, Meng‐Yao Lu, Kun Zhang, Gregory F. Cooper, Binfeng Lu, Lujia Chen, Xinghua Lu

This study uses single-cell RNA-seq data to analyze cell-cell communication changes in breast cancer patients during anti-PD-1 therapy, identifying key pathways and potential therapeutic targets.

Briefings in Bioinformatics · Mar 1, 2026Peer-reviewedBioinformatics

An interpretable deep learning framework for genome-informed precision oncology

Shuangxia Ren, Gregory F. Cooper, Lujia Chen, Xinghua Lu

This paper introduces a deep learning framework that improves drug response prediction in cancer by modeling cellular state from somatic genome alterations.

Nature Machine Intelligence · Jul 11, 2024Peer-reviewedBioinformatics

De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization

Shuangxia Ren, Yifeng Tao, Ke Yu, Yifan Xue, Russell S. Schwartz, Xinghua Lu

This paper introduces DeepGRMF, a deep learning model that predicts cell-drug sensitivities by integrating drug structures, signaling impacts, and cell states, outperforming existing methods on cancer datasets.

bioRxiv · Oct 11, 2021 · PreprintBioinformatics