Author

Van Dinh Tran

King Faisal Specialist Hospital & Research CentreORCID 0000-0002-7357-4959Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Research Focus:gene regulatory network inference· 2 Approach:graph neural network· 2 Research Focus:benchmarking· 1 Assay:bulk RNA-seq· 1 Assay:ChIP-seq· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Assay:DNA methylation· 1

Frequent Co-Authors

Rolf BackofeniD· 3 shared Michael UhliD· 2 shared Adrian FritziD· 1 shared Alexander MitrofanoviD· 1 shared Anup KumariD· 1 shared Dominik RabschiD· 1 shared Florian HeyliD· 1 shared Masako Monika KaufmanniD· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 22 mutual co-author links

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3 shared papersBioinformatics

Rolf BackofenORCID

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3 papers

Recent·Most cited

CRISCross: Integration of Epigenetic Information for Deep-Learning-Based Prediction of CRISPR-Cas9 Off-Targets Across Cell Types

William Jobson Pargeter, Dominik Rabsch, Masako Monika Kaufmann, Michael Uhl, Alexander Mitrofanov, Thomas Brox, Toni Cathomen, Van Dinh Tran, Rolf Backofen

CRISCross improves CRISPR-Cas9 off-target prediction by integrating epigenetic features with sequence data across cell types, showing better performance than sequence-only models.

bioRxiv · Oct 11, 2026 · PreprintBioinformatics

PGRanker: Enhancing Phenotype Knowledge with Graph Learning for Mendelian Gene Prioritization

Ryan Koeksal, Adrian Fritz, Miriam Schmidts, Anup Kumar, Van Dinh Tran, Rolf Backofen

PGRanker improves Mendelian gene prioritization by integrating phenotype data with gene networks using graph learning, achieving higher recall and better ranking of causal genes.

bioRxiv · Oct 8, 2026 · PreprintBioinformatics

GraphProt2: A graph neural network-based method for predicting binding sites of RNA-binding proteins

Michael Uhl, Van Dinh Tran, Florian Heyl, Rolf Backofen

GraphProt2 predicts RNA-binding protein binding sites using graph neural networks, outperforming existing methods on CLIP-seq datasets.

bioRxiv · Mar 6, 2021 · PreprintBioinformatics