Author

Lovro Rabuzin

ETH ZurichORCID 0009-0008-0146-2553Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:benchmarking· 2 Research Focus:differential expression· 2 Approach:graph neural network· 2 Assay:spatial transcriptomics· 2 Research Focus:cell-cell communication· 1 Approach:contrastive learning· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Valentina BoevaiD· 3 shared Josephine YatesiD· 1 shared Konstantin Heep· 1 shared Michel Tarnow· 1 shared Mina L Xu· 1 shared Sophie SigfsteadiD· 1 shared

Activity by Year

2025-2026 · peak 2/yr

Co-Author Collaboration Network

6 top collaborators · 7 mutual co-author links

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3 shared papersBioinformatics

Valentina BoevaORCID

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3 papers

Recent·Most cited

Cancer BiologybioRxivPreprintOct 9, 2026

GRASS-MIL: graph-based representation and discovery of phenotype-associated spatial structures with multiple instance learning

Lovro Rabuzin, Josephine Yates, Mina L Xu, Valentina Boeva

GRASS-MIL predicts phenotypes from spatial single-cell data and decomposes predictions into specific cellular neighborhoods, showing improved performance over composition-based methods.

DOI 10.64898/2026.10.08.757722

humanspatial transcriptomics

𝕏 PostBluesky

BioinformaticsbioRxivPreprintNov 27, 2025

DeepCAST-GWAS: Improving the Discovery of Genetic Associations Using Deep Learning-Based Regulatory SNP Prioritization

Lovro Rabuzin, Konstantin Heep, Sophie Sigfstead, Valentina Boeva

DeepCAST-GWAS improves GWAS by using deep learning to prioritize regulatory SNPs, increasing the reliability and replicability of genetic associations.

DOI 10.1101/2025.11.27.690924

GWAS summary statisticsdeep learning

𝕏 PostBluesky

BioinformaticsbioRxivPreprintSep 23, 2025

Exploring Augmentation-Driven Invariances for Graph Self-supervised Learning in Spatial Omics

Lovro Rabuzin, Michel Tarnow, Valentina Boeva

This study evaluates how different graph augmentations affect embedding quality and downstream performance in spatial omics, showing that well-designed augmentations improve results while poor ones can harm performance.

1 citation

spatial transcriptomicsgraph neural network

𝕏 PostBluesky