Author

N. Lucarelli

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· 1 Pathology· 1

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 1 Assay:spatial transcriptomics· 1

Frequent Co-Authors

P. Sarder· 2 shared S. Jain· 2 shared A. Naglah· 1 shared A. Paul· 1 shared A. Z. Rosenberg· 1 shared B. Ginley· 1 shared D. Manthey· 1 shared G. Nadkarni· 1 shared

Activity by Year

2023-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 33 mutual co-author links

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2 shared papersPathology

P. Sarder

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2 papers

Recent·Most cited

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology

S. Border, R. Melo Ferreira, N. Lucarelli, D. Manthey, S. Kumar, A. Paul, S. Mimar, A. Naglah, Y.-H. Cheng, L. Barisoni, J. Ray, Y. Strekalova, A. Z. Rosenberg, J. E. Tomaszewski, J. B. Hodgin, , P. Sarder

FUSION is a web-based tool that integrates spatial -omics data with histology to analyze tissue states and identify spatial linkages between molecular and histopathologic features.

bioRxiv · Jul 9, 2024 · PreprintPathology

Correlating Deep Learning-Based Automated Reference Kidney Histomorphometry with Patient Demographics and Creatinine

B. Ginley, N. Lucarelli, J. Zee, S. Jain, S. S. Han, L. Rodrigues, T. Ozrazgat-Baslanti, M. L. Wong, G. Nadkarni, K.-Y. Jen, P. Sarder

This paper uses deep learning to automate kidney histomorphometry and finds significant correlations between morphometric features and patient age, sex, and creatinine levels.

bioRxiv · May 18, 2023Cell Biology