Author

D. Paull

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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Cell Biology· 3 Pathology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Organism:human· 3 Assay:imaging· 3 Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Research Focus:drug response / drug discovery· 2 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

B. Fischbacher· 2 shared B. Migliori· 2 shared C. J. Hunter· 2 shared j. Goldberg· 2 shared S. A. Noggle· 2 shared T. Rusielewicz· 2 shared A. Daigavane· 1 shared a. Duren-Lubancki· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 35 mutual co-author links

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2 shared papersCell Biology

B. Fischbacher

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Author profile →

4 papers

Recent·Most cited

A Highly-Efficient, Scalable Pipeline for Fixed Feature Extraction from Large-Scale High-Content Imaging Screens

G. Comolet, N. Bose, J. M. Winchell, a. Duren-Lubancki, T. Rusielewicz, g. Horn, j. Goldberg, D. Paull, B. Migliori

ScaleFExSM is a scalable pipeline for extracting interpretable features from high-content imaging data, improving drug response analysis and disease profiling.

bioRxiv · Jul 6, 2023Cell Biology

Modeling gene x environment interactions in PTSD using glucocorticoid-induced transcriptomics in human neurons

M. S. Breen, T. Rusielewicz, H. N. Bader, C. Seah, C. Xu, C. J. Hunter, B. McCarthy, M. Chattopadhyay, F. Desarnaud, I. Makotkine, J. D. Flory, L. M. Bierer, M. Staniskyte, NYSCF Global Array Team, S. A. Noggle, , R. Yehuda

The paper examines gene x environment interactions in PTSD by analyzing glucocorticoid-induced transcriptomics in human neurons and blood cells from veterans with and without PTSD.

bioRxiv · Mar 1, 2021 · PreprintPathology

Modular deep learning enables automated identification of monoclonal cell lines

B. Fischbacher, S. Hedaya, B. J. Hartley, Z. Wang, G. Lallos, D. Hutson, M. Zimmer, J. Brammer, The NYSCF Global Stem Cell Array Team, D. Paull

This paper introduces Monoqlo, a deep learning workflow that automatically detects colony presence and identifies clonality from cellular imaging, enabling scalable and standardized monoclonalization.

bioRxiv · Dec 28, 2020Cell Biology

Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts

L. Schiff, B. Migliori, Y. Chen, D. Carter, C. Bonilla, J. Hall, M. Fan, E. Tam, S. Ahadi, B. Fischbacher, A. Geraschenko, C. J. Hunter, S. Venugopalan, S. DesMarteau, A. Narayanaswamy, , Yang

This study develops a platform combining deep learning and high-content imaging to identify disease-specific phenotypes in Parkinson's fibroblasts, achieving high accuracy in distinguishing disease subtypes.

bioRxiv · Nov 13, 2020Cell Biology