Author

Bastian Dislich

University of BernORCID 0000-0002-4838-4686Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2 Approach:transformer / foundation model· 2 Assay:bulk RNA-seq· 1 Research Focus:drug response / drug discovery· 1 Approach:genetic perturbation / CRISPR· 1

Frequent Co-Authors

Daniel TruhniD· 2 shared Jakob Nikolas KatheriD· 2 shared Narmin Ghaffari LalehiD· 2 shared Peter BooriD· 2 shared Roman David Buelow· 2 shared Rupert LangeriD· 2 shared Volkmar SchulziD· 2 shared Aavash Baral· 1 shared

Activity by Year

2021-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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2 shared papersBioinformatics

Daniel TruhnORCID

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3 papers

Recent·Most cited

Aurora kinase A is a synthetic lethal target in FANCA-deficient cancers

Dilara Akhoundova, Martín González Fernández, Aavash Baral, Andrej Benjak, Carmen Perry, Dinda Shezaria Hardy Lubis, Ladina Hörtensteiner, Saskia Hussung, Lea Lingg, Sina Maletti, Aino Paasinen-Sohns, Marika Lehner, Bastian Dislich, Erik Vassella, Simone de Brot, , Mark A. Rubin

The paper identifies Aurora kinase A as a synthetic lethal target in FANCA-deficient cancers and shows AURKA inhibition induces chromosomal instability and G2/M arrest in these cells.

npj Precision Oncology · Jul 11, 2026Peer-reviewedCancer Biology

Adversarial attacks and adversarial robustness in computational pathology

Narmin Ghaffari Laleh, Daniel Truhn, Gregory Patrick Veldhuizen, Tianyu Han, Marko van Treeck, Roman David Buelow, Rupert Langer, Bastian Dislich, Peter Boor, Volkmar Schulz, Jakob Nikolas Kather

This paper evaluates adversarial robustness of CNNs and ViTs in computational pathology, finding ViTs are more robust to attacks.

bioRxiv · Mar 15, 2022 · PreprintBioinformatics

Benchmarking artificial intelligence methods for end-to-end computational pathology

Narmin Ghaffari Laleh, Hannah Sophie Muti, Chiara Maria Lavinia Loeffler, Amelie Echle, Oliver Lester Saldanha, Faisal Mahmood, Ming Y. Lu, Christian Trautwein, Rupert Langer, Bastian Dislich, Roman David Buelow, Heike Irmgard Grabsch, Hermann Brenner, Jenny C. Chang-Claude, Elizabeth Alwers, , Jakob Nikolas Kather

This study benchmarks AI methods for end-to-end computational pathology, showing ResNet and ViT outperform MIL and CLAM in mutation prediction but not in tumor subtyping.

bioRxiv · Aug 9, 2021 · PreprintBioinformatics