Author

S. E. A. Raza

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 Cancer 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 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:host-pathogen & immune response· 1

Frequent Co-Authors

Y. Yuan· 2 shared A. Akarca· 1 shared A. Furness· 1 shared A. Hall· 1 shared A. Pennycuick· 1 shared A. Shephard· 1 shared B. Carroll· 1 shared B. Gusterson· 1 shared

Activity by Year

2019-2021 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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2 shared papersCancer Biology

Y. Yuan

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Author profile →

3 papers

Recent·Most cited

TIAToolbox: An End-to-End Toolbox for Advanced Tissue Image Analytics

J. Pocock, S. Graham, Q. D. Vu, M. Jahanifar, S. Deshpande, G. Hadjigeorghiou, A. Shephard, R. M. S. Bashir, M. Bilal, W. Lu, D. Epstein, F. Minhas, N. Rajpoot, S. E. A. Raza

TIAToolbox provides an end-to-end Python toolbox for computational pathology, enabling efficient and accessible use of deep learning algorithms for tissue image analysis.

bioRxiv · Dec 23, 2021Bioengineering

Immune surveillance in clinical regression of pre-invasive squamous cell lung cancer

A. Pennycuick, V. H. Teixeira, K. AbdulJabbar, S. E. A. Raza, T. Lund, A. Akarca, R. Rosenthal, C. P. Pipinikas, H. Lee-Six, D. P. Chandrasekharan, R. E. Hynds, K. H. C. Gowers, J. Y. Henry, C. Denais, M. Falzon, , S. M. Janes

The paper investigates immune surveillance in pre-invasive squamous cell lung cancer, finding that regressive lesions have more immune infiltration and distinct molecular profiles compared to progressive lesions.

bioRxiv · Nov 10, 2019Cancer Biology

Unmasking the tissue microecology of ductal carcinoma in situ with deep learning

P. L. Narayanan, S. E. A. Raza, A. Hall, J. R. Marks, L. King, R. B. West, L. Hernandez, M. Dowsett, B. Gusterson, C. Maley, S. E. Hwang, Y. Yuan

This paper develops a deep learning pipeline to detect and segment DCIS ducts, revealing that pure DCIS has more TILs but less co-localization with ducts compared to adjacent DCIS.

bioRxiv · Oct 28, 2019Pathology