Rebecka J. Jörnsten

This paper introduces a PPI prior to guide autoencoder embeddings, improving biological coherence and downstream task performance in multi-omic data.

This study uses deep autoencoders to analyze DNA methylation data and identifies disease and risk factor signatures related to aging, smoking, and disease.

The paper develops methods to generate context-specific genome-scale metabolic models from single-cell RNA-Seq data and shows that cell subtypes have unique metabolic profiles.

LASSIM infers genome-wide gene regulatory networks using mechanistic ODE models, demonstrating its effectiveness in modeling Th2 differentiation with integrated data and siRNA experiments.