Author

Christina Ren

Stanford 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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Genetic & Genomic Medicine· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:drug response / drug discovery· 2 Assay:GWAS summary statistics· 2 Approach:statistical model· 2 Research Focus:benchmarking· 1 Approach:deep learning· 1 Research Focus:polygenic risk· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

Ahmed El-kallinyiD· 1 shared Alexis KaushanskyiD· 1 shared Ali TorkamaniiD· 1 shared André Nicolás LeóniD· 1 shared Arjun Ghoshal· 1 shared Bhuvan Molparia· 1 shared Brian Schrader· 1 shared Brianna Fernandez· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 36 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Ahmed El-kallinyORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

A family-aware All of Us benchmark for polygenic prediction and genomic machine learning

Greycen Ren, Kaiden Wu, Christina Ren, Irura Nyiha, Kendall Wu, John Yang

The paper introduces a family-aware benchmark for polygenic prediction using All of Us data, enabling comparison of PRS and machine learning models across 14 traits with strong predictive performance.

medRxiv · Oct 26, 2026 · PreprintGenetic & Genomic Medicine

A Machine Learning- 3D Microvessel Platform Identifies Kinase Targets Restoring Blood-Brain-Barrier Endothelial Integrity.

Yu Jung Shin, Ling Wei, Sumin Hong, Christina Ren, M. Zigman Jeffrey, Jessica Cardenas, Harry Higgins, Nhi Ho, Kira M. Evitts, Joseph D. Smith, Jing‐fei Dong, Alexis Kaushansky, Ying Yi Zheng

This paper develops a 3D human brain endothelial platform combined with machine learning to identify kinase inhibitors that restore blood-brain barrier integrity in TBI.

bioRxiv · Dec 15, 2025 · PreprintBioengineering

Response to Polygenic Risk: Results of the MyGeneRank Mobile Application-Based Coronary Artery Disease Study

Evan D. Muse, Shang‐Fu Chen, Shuchen Liu, Brianna Fernandez, Brian Schrader, Bhuvan Molparia, André Nicolás León, Raymond Lee, Neha Pubbi, Nolan Mejia, Christina Ren, Ahmed El-kalliny, Ernesto Prado Montes de, Hector E. Tamez, Arjun Ghoshal, , Ali Torkamani

This study evaluates the impact of coronary artery disease polygenic risk scores on lipid-lowering therapy initiation via a mobile application, finding higher therapy rates among high-risk individuals.

npj Digital Medicine · Mar 11, 2022Peer-reviewedGenetic & Genomic Medicine