Author

N. Rajpoot

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 Bioinformatics· 1 Cancer Biology· 1 Pathology· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

L. Young· 3 shared A. Ben-Hur· 2 shared F. Minhas· 2 shared F. u. A. A. Minhas· 2 shared K. Branson· 2 shared L. Jones· 2 shared M. Dawood· 2 shared M. Jahanifar· 2 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 34 mutual co-author links

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3 shared papersCancer Biology

L. Young

Mutual co-authors in cluster

Author profile →

4 papers

Recent·Most cited

Cancer drug sensitivity prediction from routine histology images

M. Dawood, Q. D. Vu, L. Young, K. Branson, L. Jones, N. Rajpoot, F. u. A. A. Minhas

This paper demonstrates using deep learning to predict cancer drug sensitivity from histology images and cell line data, identifying histological patterns linked to drug responses.

bioRxiv · Jun 3, 2023Cancer Biology

Data-Driven Modelling of Gene Expression States in Breast Cancer and their Prediction from Routine Whole Slide Images

M. Dawood, M. Eastwood, M. Jahanifar, L. Young, A. Ben-Hur, K. Branson, L. Jones, N. Rajpoot, F. u. A. A. Minhas

The paper predicts gene expression states from whole slide images using a graph neural network and identifies co-expressing gene groups with clinical and therapeutic relevance.

bioRxiv · Apr 14, 2023 · PreprintPathology

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

Improving COVID-19 Testing Efficiency using Guided Agglomerative Sampling

F. Minhas, D. Grammatopoulos, L. Young, I. Amin, D. Snead, N. Anderson, A. Ben-Hur, N. Rajpoot

This paper proposes agglomerative sampling to improve testing efficiency for low-prevalence COVID-19 populations, achieving ~40% test savings in simulations.

bioRxiv · Apr 13, 2020 · PreprintBioinformatics