Author

C. D. Rudin

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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Microbiology· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Organism:human· 2 Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1 Research Focus:clustering· 1 Approach:deep learning· 1

Frequent Co-Authors

D. M. Margolis· 2 shared D. M. Murdoch· 2 shared E. P. Browne· 2 shared A. D. Cooper-Volkheimer· 1 shared A. Manickam· 1 shared A. Oesterling· 1 shared J. J. Peterson· 1 shared L. Semenova· 1 shared

Activity by Year

2022-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersMicrobiology

D. M. Margolis

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy.

L. Semenova, Y. Wang, S. D. Falcinelli, N. Archin, A. D. Cooper-Volkheimer, N. Goonetilleke, D. M. Murdoch, C. D. Rudin, D. M. Margolis, E. P. Browne

The paper uses machine learning to analyze HIV reservoir and immunology data to identify immune signatures associated with total and intact HIV DNA levels during ART.

bioRxiv · Nov 16, 2023Microbiology

Integrated single-cell multiomic analysis of HIV latency reversal reveals novel regulators of viral reactivation.

A. Manickam, J. J. Peterson, Y. Harigaya, D. M. Murdoch, D. M. Margolis, A. Oesterling, Z. Guo, C. D. Rudin, Y. Jiang, E. P. Browne

The paper uses single-cell RNA-seq and ATAC-seq to analyze HIV latency reversal, identifying GATA3 as a novel regulator and demonstrating a machine learning model's predictive accuracy.

bioRxiv · Jul 26, 2022 · PreprintMicrobiology