Author

T. D. Capellini

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 Cell Biology· 1 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:human· 3 Research Focus:cell signaling & trafficking· 2 Approach:experimental assay· 2 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:development & morphogenesis· 1 Research Focus:differential expression· 1

Frequent Co-Authors

A. Banerjee· 1 shared A. C. Doxey· 1 shared A. G. McArthur· 1 shared A. Guermazi· 1 shared A. J. McGeer· 1 shared A. T. Irving· 1 shared B. J.- M. Tremblay· 1 shared B. Wang· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 28 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperImmunology

A. Banerjee

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Cell BiologybioRxivFeb 2, 2022

A quiescent resident progenitor pool is the central organizer of tendon healing

M. Grinstein, S. Tsai, D. Montoro…·J. L. Galloway

This study identifies Axin2+ tenocytes as quiescent progenitors critical for tendon healing, showing they activate and organize repair after injury.

DOI 10.1038/s41536-024-00370-2

𝕏 PostBluesky

ImmunologybioRxivJun 18, 2020

Experimental and natural evidence of SARS-CoV-2 infection-induced activation of type I interferon responses

A. Banerjee, N. El-Sayes, P. Budylowski…·K. Mossman

This study shows SARS-CoV-2 infection induces a mild type I IFN response in vitro and in moderate COVID-19 cases, and cannot inhibit exogenous IFN induction.

DOI 10.1016/j.isci.2021.102477

humanbulk RNA-seq

𝕏 PostBluesky

BioengineeringbioRxivNov 6, 2018

Pairwise learning of MRI scans using a convolutional Siamese network for prediction of knee pain

G. H. Chang, D. T. Felson, S. Qiu…·Vijaya B Kolachalama

This paper uses a convolutional Siamese network to predict bilateral knee pain from MRI scans, achieving 70.1% mean accuracy.

DOI 10.1007/s00330-020-06658-3

𝕏 PostBluesky