Author

M. Jahanifar

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 Pathology· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

N. Rajpoot· 2 shared A. Ben-Hur· 1 shared A. Shephard· 1 shared D. Epstein· 1 shared F. Minhas· 1 shared F. u. A. A. Minhas· 1 shared G. Hadjigeorghiou· 1 shared J. Pocock· 1 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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2 shared papersPathology

N. Rajpoot

Mutual co-authors in cluster

Author profile →

2 papers

Recent·Most cited

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