Author

K. Banu

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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Pathology· 2

Signature Assays, Organisms & Methods

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Research Focus:cell type annotation· 2 Research Focus:differential expression· 2 Assay:imaging· 2 Research Focus:spatial domain detection· 2 Research Focus:clustering· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Organism:human· 1

Frequent Co-Authors

M. C. Menon· 2 shared Q. Lin· 2 shared A. C. Reghuvaran· 1 shared B. Murphy· 1 shared C. Wei· 1 shared C. Xi· 1 shared D. G. Moledina· 1 shared F. P. Wilson· 1 shared

Activity by Year

2021-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersPathology

M. C. Menon

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2 papers

Recent·Most cited

Comparative evaluation of glomerular morphometric techniques reveals differential technical artefacts between FSGS and normal glomeruli

J. M. Basgen, A. C. Reghuvaran, Q. Lin, K. Banu, H. Shi, J. Pell, J. DiRito, G. T. Tietjen, S. Perincheri, D. G. Moledina, F. P. Wilson, M. C. Menon

This study evaluates glomerular morphometric techniques, finding that 10-glomerular sampling is sufficiently precise and that paraffin-embedding causes shrinkage bias affecting FSGS and normal glomeruli differently.

bioRxiv · Feb 15, 2022 · PreprintPathology

Deep learning identifies pathological abnormalities predictive of graft loss in kidney transplant biopsies

Z. Yi, F. Salem, M. C. Menon, K. Keung, C. Xi, S. Hultin, M. R. H. A. Rasheed, L. Li, F. Su, Z. Sun, C. Wei, W. Huang, S. Fredericks, Q. Lin, K. Banu, , W. Zhang

This paper develops a deep learning model to quantify pathological features in kidney transplant biopsies, showing improved prediction of graft loss compared to clinical and Banff scores.

bioRxiv · Apr 18, 2021Pathology