Author

M. Fatemi

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· 3

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:spatial transcriptomics· 3 Research Focus:cell-cell communication· 2 Research Focus:clustering· 2 Approach:deep learning· 2 Assay:imaging· 2 Research Focus:cancer & tumor microenvironment· 1 Research Focus:cell type annotation· 1

Frequent Co-Authors

B. Christensen· 3 shared F. Kolling· 3 shared J. Levy· 3 shared L. Vaickus· 3 shared S. Palisoul· 3 shared Z. Azher· 3 shared A. Diallo· 2 shared G. Srinivasan· 2 shared

Activity by Year

2022-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersPathology

B. Christensen

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Author profile →

3 papers

Recent·Most cited

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining

G. Srinivasan, M. Davis, M. LeBoeuf, M. Fatemi, Z. Azher, Y. Lu, A. Diallo, M. Montivero, F. Kolling, L. Perrard, L. Salas, B. Christensen, S. Palisoul, G. Tsongalis, L. Vaickus, , J. Levy

This paper develops machine learning models to infer spatial transcriptomics data from H&E-stained skin slides, improving the assessment of photoaging and its therapeutic implications.

bioRxiv · Jul 30, 2023 · PreprintPathology

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis

Z. Azher, M. Fatemi, Y. Lu, G. Srinivasan, A. Diallo, B. Christensen, L. Salas, F. Kolling, L. Perreard, S. Palisoul, L. Vaickus, J. Levy

This paper applies spatial transcriptomics data with contrastive crossmodal pretraining to improve graph-based deep learning models for cancer histopathology analysis.

bioRxiv · Jul 30, 2023 · PreprintPathology

Inferring Spatially Resolved Transcriptomics Data from Whole Slide Images for the Assessment of Colorectal Tumor Metastasis: A Feasibility Study

M. Fatemi, E. Feng, C. Sharma, Z. Azher, T. Goel, O. Ramwala, S. Palisoul, R. Barney, L. Perreard, F. Kolling, L. A. Salas, B. Christensen, G. Tsongalis, L. Vaickus, J. Levy

This study investigates using deep learning to predict spatial transcriptomics data from whole slide images to assess colorectal tumor metastasis, finding that convolutional networks outperform transformer and graph-based models.

bioRxiv · Nov 24, 2022 · PreprintPathology