Author

Frank Dondelinger

Lancaster UniversityORCID 0000-0003-1816-6300Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

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Assay:bulk RNA-seq· 3 Research Focus:differential expression· 3 Approach:statistical model· 3 Organism:human· 1

Frequent Co-Authors

Dennis Y.Q. WangiD· 1 shared Evanthia KoukouliiD· 1 shared Juhyun ParkiD· 1 shared Sach Mukherjee· 1 shared Sanjay Curtis NagiiD· 1 shared Sara Elg· 1 shared The Alzheimer’s Disease Neuroimaging Initiative· 1 shared Victoria Anne InghamiD· 1 shared

Activity by Year

2016-2021 · peak 2/yr

Co-Author Collaboration Network

8 top collaborators · 7 mutual co-author links

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1 shared paperBioinformatics

Dennis Y.Q. WangORCID

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3 papers

Recent·Most cited

BioinformaticsPLoS Computational BiologyPeer-reviewedJan 25, 2021

A regularized functional regression model enabling transcriptome-wide dosage-dependent association study of cancer drug response

Evanthia Koukouli, Dennis Y.Q. Wang, Frank Dondelinger, Juhyun Park

The paper develops a regularized functional regression model to identify dosage-dependent gene expression associations with cancer drug response using in vitro data.

6 citations

bulk RNA-seqstatistical model

𝕏 PostBluesky

Systems BiologyCurrent Research in Insect SciencePeer-reviewedJan 1, 2021

Capturing the transcription factor interactome in response to sub-lethal insecticide exposure

Victoria Anne Ingham, Sara Elg, Sanjay Curtis Nagi, Frank Dondelinger

This study uses dynamic Bayesian networks to analyze transcriptome time-course data from sub-lethal pyrethroid-exposed Anopheles coluzzii, identifying 44 transcription factors involved in pesticide response.

11 citations

bulk RNA-seqstatistical model

𝕏 PostBluesky

BioinformaticsbioRxivPreprintDec 9, 2016

High-dimensional regression over disease subgroups

Frank Dondelinger, Sach Mukherjee, The Alzheimer’s Disease Neuroimaging Initiative

This paper develops a high-dimensional regression method for disease subgroups, showing improved prediction and subgroup-specific sparsity estimation using Alzheimer's, ALS, and cancer data.

1 citation

humanbulk RNA-seq

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