Author

T. Sakellaropoulos

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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Cancer Biology· 2 Pathology· 1

Signature Assays, Organisms & Methods

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Approach:deep learning· 3 Research Focus:differential expression· 3 Organism:human· 3 Research Focus:cell type annotation· 2 Research Focus:gene regulatory network inference· 2 Assay:imaging· 2 Assay:bulk RNA-seq· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

A. Tsirigos· 3 shared D. Fenyo· 2 shared N. Coudray· 2 shared N. Razavian· 2 shared A. Hatzimemos· 1 shared A. Heguy· 1 shared A. L. Moreira· 1 shared C. Do· 1 shared

Activity by Year

2017-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 25 mutual co-author links

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3 shared papersCancer Biology

A. Tsirigos

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Author profile →

3 papers

Recent·Most cited

MethNet: a robust approach to identify regulatory hubs and their distal targets in cancer

T. Sakellaropoulos, C. Do, G. Jiang, G. Cova, P. Meyn, D. Dimartino, S. Ramaswami, A. Heguy, A. Tsirigos, J. A. Skok

MethNet identifies regulatory hubs and their distal targets in cancer by integrating DNA methylation and gene expression data to uncover cis regulatory elements linked to patient survival.

bioRxiv · Jul 7, 2023 · PreprintCancer Biology

A Deep Learning Approach for Rapid Mutational Screening in Melanoma

R. Kim, S. Nomikou, N. Coudray, G. Jour, Z. Dawood, R. Hong, E. Esteva, T. Sakellaropoulos, D. Donnelly, U. Moran, A. Hatzimemos, J. S. Weber, N. Razavian, I. Aifantis, D. Fenyo, , A. Tsirigos

This paper develops a deep learning model to detect BRAF mutations in melanoma histopathology images using whole slide images from 256 patients, achieving AUC scores of 0.96 and 0.72.

bioRxiv · Aug 19, 2020 · PreprintPathology

Classification and Mutation Prediction from Non-Small Cell Lung Cancer Histopathology Images using Deep Learning

N. Coudray, A. L. Moreira, T. Sakellaropoulos, D. Fenyo, N. Razavian, A. Tsirigos

This study uses deep learning to classify lung cancer subtypes and predict gene mutations from histopathology images, achieving high accuracy and outperforming human pathologists.

bioRxiv · Oct 3, 2017Cancer Biology