Author

Helmut Frieß

Technical University of Munich

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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Bioinformatics· 1

Signature Assays, Organisms & Methods

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

Frequent Co-Authors

Alexander MuckenhuberiD· 1 shared Ernst J. RummenyiD· 1 shared Fabian Karl LohöferiD· 1 shared Georgios A. KaissisiD· 1 shared Hana AlgüliD· 1 shared Hsi-Yu YeniD· 1 shared Jens Thomas SivekeiD· 1 shared Katja SteigeriD· 1 shared

Activity by Year

2019-2019 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperBioinformatics

Alexander MuckenhuberORCID

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1 paper

Recent·Most cited

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, Katja Steiger, Hana Algül, Alexander Muckenhuber, Hsi-Yu Yen, Ernst J. Rummeny, Helmut Frieß, Roland Michael Schmid, Wilko Weichert, Jens Thomas Siveke, 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.

PLoS ONE · Oct 2, 2019Peer-reviewedBioinformatics