Timo Verlaan

A critical evaluation of Gene Ontology priors in biologically-informed neural networks

This study evaluates the impact of Gene Ontology priors in biologically-informed neural networks using RNA-seq data, finding limited performance improvement and interpretability beyond predefined gene sets.

scAGG: Sample-level embedding and classification of Alzheimer's disease from single-nucleus data

This paper introduces scAGG, a model for classifying Alzheimer's disease from single-nucleus RNA-seq data by aggregating single-cell embeddings, and finds it accurately classifies AD and healthy controls with disease severity ordering in the latent space.