Robin B. Gasser

HyperSTAR identifies spatial domains across resolutions using hypergraph learning, outperforming existing methods in spatial omics analysis and revealing breast cancer spatial heterogeneity.

This paper presents a chromosome-level genome and genomic dataset for the invasive mite Halotydeus destructor, identifies pesticide resistance mutations, and develops KASP markers for resistance diagnostics.

This study identifies novel Cryptosporidium subtypes and genetic diversity linked to swimming pool outbreaks in Victoria, Australia, using molecular surveillance and epidemiological data.

SCREEN is a graph neural network that predicts catalytic residues and assesses mutation tolerance in enzymes by integrating structural and functional information.

scRCA improves cell type annotation using noisy reference data by combining a Siamese network with noise-robust loss functions and outperforms existing methods.

This paper introduces scpDeconv, a deep learning method for deconvolving cell type mixtures in proteomic data, showing improved performance across species and technologies.

The paper presents the first reference genome of the redlegged earth mite, identifying genes associated with pesticide resistance and population structure in its invasive range.

The paper analyzes gene and protein domain content changes across 76 arthropod genomes to understand evolutionary patterns and their association with phenotypic adaptations.

This study analyzes the genome of Colletotrichum tanaceti to identify pathogenicity genes and reveals rapid evolution in CAZyme families and a 'two-speed' genome structure.