Author

C. Shin

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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Bioengineering· 1 Cancer Biology· 1 Scientific Communication & Education· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:cell-cell communication· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Assay:metagenomics· 1 Assay:scRNA-seq· 1

Frequent Co-Authors

A. Aggarwal· 1 shared A. Angelova· 1 shared C. J. Wu· 1 shared C. Mangum· 1 shared C. Park· 1 shared D. Abbondanza· 1 shared D. Hurt· 1 shared D. S. Neuberg· 1 shared

Activity by Year

2021-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 18 mutual co-author links

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1 shared paperBioengineering

A. Aggarwal

Author profile →

3 papers

Recent·Most cited

Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy

K. Maurer, C. Park, S. Mani, M. Borji, L. Penter, Y. Jin, J. Y. Zhang, C. Shin, J. R. Brenner, J. Southard, S. Krishna, W. Lu, H. Lyu, D. Abbondanza, C. Mangum, , E. Azizi

The paper analyzes single-cell data from bone marrow samples to identify immune cell interactions that predict response to adoptive cellular therapy in myeloid leukemia.

bioRxiv · Feb 9, 2024 · PreprintCancer Biology

Machine Learning Elucidates Design Features of Plasmid DNA Lipid Nanoparticles for Cell Type-Preferential Transfection

L. Cheng, Y. Zhu, J. Ma, A. Aggarwal, W. H. Toh, C. Shin, W. Sangpachatanaruk, G. Weng, R. Kumar, H.-Q. Mao

This study uses machine learning to identify LNP composition parameters that enhance transfection efficiency across cell types and modulate cell type-preferentiality.

bioRxiv · Dec 7, 2023 · PreprintBioengineering

Current challenges in microbiome metadata collection

G. Rosenfeld, A. Angelova, C. Shin, M. Quinones, D. Hurt

The paper discusses challenges in microbiome metadata collection and emphasizes the need for standardized, high-quality metadata to ensure FAIR data practices.

bioRxiv · May 5, 2021 · PreprintScientific Communication & Education