Author

Dan Leggate

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

Click to filter author papers

Bioinformatics· 3

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:bulk RNA-seq· 3 Organism:human· 3 Research Focus:drug response / drug discovery· 2 Research Focus:gene regulatory network inference· 2 Approach:machine learning (classical)· 2 Assay:proteomics· 2 Approach:Bayesian inference· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Hendrik WeisseriD· 2 shared Adrián BazagaiD· 1 shared Andrej AlendariD· 1 shared Georgia TsagkogeorgaiD· 1 shared Helena Santos-Rosa· 1 shared Martine van den Heever· 1 shared Namshik HaniD· 1 shared Neil Wilkie· 1 shared

Activity by Year

2020-2024 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 19 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

2 shared papersBioinformatics

Hendrik WeisserORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Signaling pathway evaluation of leading ATRi, PARPi and CDK7i cancer compounds targeting the DNA Damage Response using Causal Inference

Nina Truter, Wikus Bergh, Martine van den Heever, Shade Horn, Shaw Kj, Dan Leggate, Neil Wilkie, Raminderpal Singh

This paper uses causal inference to evaluate resistance mechanisms of DDR inhibitors in cancer, identifying pathway-specific resistance drivers and potential combination therapies.

bioRxiv · Feb 15, 2024 · PreprintBioinformatics

Predicting genes associated with RNA methylation pathways using machine learning

Georgia Tsagkogeorga, Helena Santos-Rosa, Andrej Alendar, Dan Leggate, Oliver Rausch, Tony Kouzarides, Hendrik Weisser, Namshik Han

This study uses machine learning to predict genes associated with RNA methylation pathways in humans using multi-omics data from the Harmonizome database.

Communications Biology · Aug 25, 2022Peer-reviewedBioinformatics

Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology

Adrián Bazaga, Dan Leggate, Hendrik Weisser

This study uses machine learning to predict novel therapeutic targets in nine cancers by analyzing mutation, gene expression, and essentiality data.

Scientific Reports · Jul 1, 2020Peer-reviewedBioinformatics