Author

Christoph Wierling

Alacris (Germany)ORCID 0000-0002-8856-8412Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Systems Biology· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:drug response / drug discovery· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Assay:whole-exome sequencing· 1

Frequent Co-Authors

Bodo M. H. Lange· 2 shared Daniel WeindliD· 2 shared Jan HasenaueriD· 2 shared Leonard SchmiesteriD· 2 shared Alexey A. ShadriniD· 1 shared Artur MuradyaniD· 1 shared Dilan PathiranaiD· 1 shared Fabian FröhlichiD· 1 shared

Activity by Year

2018-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 40 mutual co-author links

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2 shared papersSystems Biology

Bodo M. H. Lange

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2 papers

Recent·Most cited

Systems BiologyNature CommunicationsPeer-reviewedJan 10, 2022

Mini-batch optimization enables training of ODE models on large-scale datasets

Paul L. Stapor, Leonard Schmiester, Christoph Wierling…·Jan Hasenauer

This paper applies mini-batch optimization to ODE models, improving efficiency and results on a cancer signaling dataset.

29 citations

machine learning (classical)benchmarking

𝕏 PostBluesky

Systems BiologyCell SystemsPeer-reviewedNov 28, 2018

Efficient parameterization of large-scale mechanistic models enables drug response prediction for cancer cell lines

Fabian Fröhlich, Thomas Kessler, Daniel Weindl…·Jan Hasenauer

The paper presents a computational framework to parameterize large-scale mechanistic models for predicting drug response in cancer cell lines using exome and transcriptome data, achieving higher accuracy than statistical approaches.

142 citationstop 3%

humanwhole-exome sequencing

𝕏 PostBluesky