Jonas Fischer

This paper introduces Giraffe, a matrix factorization method for gene regulatory network inference that estimates signed regulatory effects, showing improved accuracy in distinguishing activating and inhibitory interactions across multiple biological contexts.

The paper develops a Nextflow workflow for reproducibly inferring regulatory networks from TCGA data, showing its application to colon cancer subtypes.

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

FAME quantifies DNA methylation from bulk and single-cell WGBS data efficiently without intermediate files, achieving speed comparable to standard methods without loss of accuracy.