Author

Jeffrey A. Thompson

The University of Kansas Cancer CenterORCID 0000-0002-0876-2582Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Cell Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:bulk RNA-seq· 3 Research Focus:differential expression· 3 Approach:statistical model· 3 Research Focus:gene regulatory network inference· 2 Approach:biophysical modeling· 1 Research Focus:cell type annotation· 1 Assay:DNA methylation· 1 Organism:human· 1

Frequent Co-Authors

Antonio ArtiguesiD· 1 shared Aspin R. Denson· 1 shared Brock C. ChristenseniD· 1 shared Carmen J. MarsitiD· 1 shared Chad M SlawsoniD· 1 shared Dakota R. RobartsiD· 1 shared Devin C. KoestleriD· 1 shared Ee Phie TaniD· 1 shared

Activity by Year

2018-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

1 shared paperCell Biology

Antonio ArtiguesORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Multi-Omics after O-GlcNAc Alteration Identifies Cellular Processes Working Synergistically to Promote Aneuploidy

Samuel S. Boyd, Dakota R. Robarts, Khue Nguyen, Maite T. Villar, Ibtihal M. Alghusen, Manasi Kotulkar, Aspin R. Denson, Halyna Fedosyuk, Stephen A. Whelan, Norman C.Y. Lee, John A. Hanover, Wagner B. Dias, Ee Phie Tan, Steven R. McGreal, Antonio Artigues, , Chad M Slawson

The paper uses multi-omics analysis to show OGT regulates aneuploidy by fine-tuning multiple cellular pathways through O-GlcNAcylation.

Molecular Metabolism · Oct 30, 2024Peer-reviewedCell Biology

Equivalent Change Enrichment Analysis: Assessing Equivalent and Inverse Change in Biological Pathways between Diverse Experiments

Jeffrey A. Thompson, Devin C. Koestler

The paper introduces a statistical method to assess equivalent and inverse changes in biological pathways across different experiments, showing it provides robust results compared to ad hoc approaches.

BMC Genomics · Feb 24, 2020Peer-reviewedBioinformatics

Methylation-To-Expression Feature Models of Breast Cancer Accurately Predict Overall Survival, Distant-Recurrence Free Survival, And Pathologic Complete Response in Multiple Cohorts

Jeffrey A. Thompson, Brock C. Christensen, Carmen J. Marsit

The paper develops and validates M2EFM, a methylation-to-expression model, to predict breast cancer outcomes across multiple cohorts with good accuracy and clinical utility.

Scientific Reports · Mar 26, 2018Peer-reviewedBioinformatics