Leander Schietgat

The paper formalizes and addresses the computational complexity of haplotyping microbial communities using metagenomic data and provides open-source tools for the task.

This paper introduces a method to recover gene haplotypes from metagenomes and shows it accurately identifies mutation rate differences between core and accessory gene families in rumen microbiomes.

This paper introduces a probabilistic method to recover haplotypes from metagenomic data, achieving 100% accuracy in recovering cryptic gene isoforms from HIV-1 strain mix data.

The paper demonstrates that machine learning can accurately predict odor perception from molecular features, enabling reverse-engineering of smell.