Author

Sebastian Ziegelmayer

Technical University of MunichORCID 0000-0001-8724-4718Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2

Signature Assays, Organisms & Methods

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Research Focus:differential expression· 2 Organism:human· 2 Assay:imaging· 2 Approach:machine learning (classical)· 2 Research Focus:cell type annotation· 1 Research Focus:drug response / drug discovery· 1

Frequent Co-Authors

Ernst J. RummenyiD· 2 shared Fabian Karl LohöferiD· 2 shared Georgios A. KaissisiD· 2 shared Hana AlgüliD· 2 shared Jens Thomas SivekeiD· 2 shared Rickmer BrareniD· 2 shared Roland Michael SchmidiD· 2 shared Wilko WeichertiD· 2 shared

Activity by Year

2019-2019 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 44 mutual co-author links

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2 shared papersBioinformatics

Ernst J. RummenyORCID

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2 papers

Recent·Most cited

BioinformaticsEuropean Radiology ExperimentalPeer-reviewedOct 17, 2019

A prospectively validated machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma

Georgios A. Kaissis, Sebastian Ziegelmayer, Fabian Karl Lohöfer…·Rickmer Braren

The paper develops a machine learning model using radiomic features from MRI to predict survival and tumor subtype in pancreatic ductal adenocarcinoma with high accuracy.

79 citationstop 2%

humanimaging

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BioinformaticsPLoS ONEPeer-reviewedOct 2, 2019

A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy

Georgios A. Kaissis, Sebastian Ziegelmayer, Fabian Karl Lohöfer…·Rickmer Braren

This paper develops a machine learning model to predict molecular subtypes of PDAC from radiomic features, showing differential chemotherapy response based on subtype.

76 citationstop 2%

humanimaging

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