Thomas Brüls

This paper introduces a zero-inflated log-normal graphical model for microbial association network inference, showing improved performance on sparse metagenomic data.

This paper introduces a scalable pre-assembly binning method using sparse coding to recover metagenome-assembled genomes from short reads, showing it can recover low-abundance genomes effectively.

The paper introduces LSH-SNN, a clustering algorithm for high-dimensional sequence data, applied to metagenomics to reconstruct genomes from environmental samples without reference data.