Author

Regina Shaw

NYU Langone Health

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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Genomics· 3

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 3 Organism:human· 3 Assay:bulk RNA-seq· 2 Research Focus:clustering· 2 Research Focus:gene regulatory network inference· 2 Assay:scRNA-seq· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1

Frequent Co-Authors

Hemali P. PhatnaniiD· 3 shared Molly HammelliD· 3 shared Oliver H. TamiD· 3 shared Isobel Bolger· 2 shared Kathryn O’NeilliD· 2 shared Andrea MalaspinaiD· 1 shared Avindra NathiD· 1 shared Bin ZhangiD· 1 shared

Activity by Year

2019-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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3 shared papersGenomics

Hemali P. PhatnaniORCID

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3 papers

Recent·Most cited

TDP-43 dysfunction leads to the accumulation of cryptic transposable element-derived exons, crypTEs, in iPSC derived neurons and ALS/FTD patient tissues

Isobel Bolger, Regina Shaw, Oliver H. Tam, Cláudio Gouveia Roque, Christopher A. Jackson, Kathryn O’Neill, Colin M. Smith, Hemali P. Phatnani, Karthick Natarajan, Molly Hammell

This study identifies TDP-43-dependent cryptic gene-TE fusions (crypTEs) in neurons and ALS/FTD tissues, showing their association with altered gene expression and potential pathogenic effects.

bioRxiv · Jan 9, 2026 · PreprintGenomics

ALS molecular subtypes are a combination of cellular, genetic, and pathological features learned by deep multiomics classifiers

Kathryn O’Neill, Regina Shaw, Isobel Bolger, Oliver H. Tam, Hemali P. Phatnani, Molly Hammell

This study introduces DANcer, a deep learning classifier that identifies robust ALS molecular subtypes across bulk and single-cell datasets, showing they correlate with clinical features and cellular mechanisms.

Cell Reports · Mar 1, 2025Peer-reviewedGenomics

Postmortem Cortex Samples Identify Distinct Molecular Subtypes of ALS: Retrotransposon Activation, Oxidative Stress, and Activated Glia

Oliver H. Tam, Nikolay V. Rozhkov, Regina Shaw, Duyang Kim, Isabel Hubbard, Samantha Fennessey, Nadia Propp, Hemali P. Phatnani, Justin Y. Kwan, Dhruv Sareen, James Riley Broach, Zachary Simmons, Ximena Arcila-Londono, Edward B. Lee, Vivianna M. Van Deerlin, , John Ravits

The paper uses unsupervised machine learning to identify three molecular subtypes in ALS cortex samples, associated with oxidative stress, retrotransposon activation, and glial activation.

Cell Reports · Oct 1, 2019Peer-reviewedGenomics