Author

F. Dondelinger

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· 1 Ecology· 1 Microbiology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Research Focus:differential expression· 1 Research Focus:drug response / drug discovery· 1 Research Focus:gene regulatory network inference· 1

Frequent Co-Authors

A. Aderhold· 1 shared A. Sadykov· 1 shared D. Sadykova· 1 shared D. Wang· 1 shared E. Beamish· 1 shared F. Hatami· 1 shared G. Kutkaite· 1 shared GDSC Screening Team· 1 shared

Activity by Year

2020-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 13 mutual co-author links

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Curved chords = mutual co-authorship

1 shared paperEcology

A. Aderhold

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Weak interactions cause poor performance of common network inference models

D. Sadykova, J. M. Yearsley, A. Aderhold, F. Dondelinger, H. J. White, L. L. Sanchez, M. Ilic, A. Sadykov, M. Emmerson, P. Caplat

The paper evaluates four network inference methods and finds they poorly distinguish interactions in networks with weak interactions and limited data.

bioRxiv · Jun 22, 2023 · PreprintEcology

A statistical framework for assessing pharmacological response and biomarkers using uncertainty estimates

D. Wang, J. Hensman, G. Kutkaite, T. S. Toh, GDSC Screening Team, J. R. Dry, J. Saez-Rodriguez, M. Garnett, M. P. Menden, F. Dondelinger

The paper introduces a statistical framework using uncertainty estimates to identify biomarkers from drug screening data, finding 24 known and 6 novel biomarkers in cancer cell lines.

bioRxiv · May 1, 2020 · PreprintCancer Biology

A Bayesian Multi-Task Approach for Detecting Global Microbiome Associations

F. Hatami, E. Beamish, R. Rigby, F. Dondelinger

This paper introduces a Bayesian multi-task method to detect global microbiome associations without relying on distance measures or individual species analysis, showing reliable performance on real-world datasets.

bioRxiv · Jan 8, 2020 · PreprintMicrobiology