Author

P. P. Chen

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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Immunology· 2 Bioengineering· 1

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:gene regulatory network inference· 2 Approach:machine learning (classical)· 2 Assay:scRNA-seq· 2 Research Focus:cell-cell communication· 1 Assay:clinical cohort / trial· 1

Frequent Co-Authors

A.-M. Cepika· 2 shared B. C. Thomas· 2 shared C. Waichler· 2 shared M. G. Roncarolo· 2 shared R. A. Freeborn· 2 shared R. Bacchetta· 2 shared A. J. Gentles· 1 shared A. P. Boss· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersImmunology

A.-M. Cepika

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Author profile →

3 papers

Recent·Most cited

Epigenetic signature and key transcriptional regulators of human antigen-specific type 1 regulatory T cells

A.-M. Cepika, L. Amaya, C. Waichler, M. Narula, M. M. Mantilla, B. C. Thomas, P. P. Chen, R. A. Freeborn, M. Pavel-Dinu, J. Nideffer, M. Porteus, R. Bacchetta, F. Mueller, W. J. Greenleaf, H. Y. Chang, M. G. Roncarolo

This study identifies the epigenetic signature and key transcription factors regulating human antigen-specific Tr1 cells, revealing their unique biology and potential for therapeutic applications.

bioRxiv · Mar 7, 2024 · PreprintImmunology

AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML

E. C. Sayitoglu, B. A. Luca, A. P. Boss, B. C. Thomas, R. A. Freeborn, M. J. Uyeda, P. P. Chen, Y. Nakauchi, C. Waichler, N. J. Lacayo, R. Bacchetta, R. Majeti, A. J. Gentles, A.-M. Cepika, M. G. Roncarolo

The paper identifies ICAM1 as a key factor in T cell killing of AML by analyzing transcriptional programs in AML and T cell interactions.

bioRxiv · Sep 21, 2023Immunology

Automated detection of glaucoma with interpretable machine learning using clinical data and multi-modal retinal images

P. Mehta, C. Petersen, J. C. Wen, M. R. Banitt, P. P. Chen, K. D. Bojikian, C. Egan, S.-I. Lee, M. Balazinska, A. Y. Lee, A. Rokem, The UK Biobank Eye and Vision Consortium

This paper develops an interpretable machine learning model for glaucoma detection using clinical data and multi-modal retinal images, achieving high accuracy and identifying both known and novel disease-related features.

bioRxiv · Feb 26, 2020Bioengineering