Joachim Johansen

CHAMP provides accurate taxonomic profiling of prokaryotes, eukaryotes, and viruses in the human microbiome, outperforming existing tools in recall, F1 score, and false positive reduction.

This paper introduces AAMB, a deep learning method for metagenomic binning that improves genome reconstruction by integrating sequence co-abundances and tetranucleotide frequencies, outperforming existing methods in completeness and diversity.

The paper develops PHAMB, a method to bin viral genomes from bulk metagenomics data, and applies it to the HMP2 cohort to recover viral genomes and explore viral-host interactions.

This paper introduces a deep learning method using variational autoencoders to improve microbial genome binning from metagenomic data, achieving higher precision and completeness in reconstructed bins.

This study investigates the association between genetic variants in PMAT and OCT1 transporters and metformin gastrointestinal intolerance in type 2 diabetes patients.