Author

C. van Herpen

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

Signature Assays, Organisms & Methods

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Organism:human· 2 Assay:whole-genome sequencing· 2 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Approach:experimental assay· 1 Research Focus:genome assembly· 1 Research Focus:variant calling· 1 Research Focus:variant effect prediction· 1

Frequent Co-Authors

E. Cuppen· 2 shared N. Steeghs· 2 shared A. Danyi· 1 shared A. van Hoeck· 1 shared C. Shale· 1 shared E. de Bruijn· 1 shared E. Smit· 1 shared E. Voest· 1 shared

Activity by Year

2019-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersCancer Biology

E. Cuppen

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2 papers

Recent·Most cited

Pan-cancer whole genome analyses of metastatic solid tumors

P. Priestley, J. Baber, M. Lolkema, N. Steeghs, E. de Bruijn, K. Duyvesteyn, S. Haidari, A. van Hoeck, W. Onstenk, P. Roepman, C. Shale, M. Voda, H. Bloemendal, V. Tjan-Heijnen, C. van Herpen, , E. Cuppen

This study analyzes whole genome sequences of metastatic solid tumors to identify genetic variants that may influence treatment outcomes.

bioRxiv · Aug 12, 2019 · PreprintCancer Biology

A deep learning system can accurately classify primary and metastatic cancers based on patterns of passenger mutations

W. Jiao, G. Atwal, P. Polak, R. Karlic, E. Cuppen, A. Danyi, J. de Ridder, C. van Herpen, M. P. Lolkema, N. Steeghs, G. Getz, Q. D. Morris, L. D. Stein, PCAWG Pathology & Clinical Correlates Working Grp, ICGC/TCGA Pan-cancer Analysis of Whole Genomes Net

This paper develops a deep learning classifier using whole genome sequencing data to distinguish cancer types and identify metastatic origins.

bioRxiv · Jan 22, 2019 · PreprintCancer Biology