Metagenome-assembled genomes (MAGs) provide insights into microbial dark matter, but contamination remains a concern for downstream analysis. Zou et al. develop a multi-modal deep language model that leverages microbial sequences to remove ‘unexpected’ contigs from MAGs. This approach is compatible with any contig binning tools and increases the number of high-quality bins.
- Bohao Zou
- Jingjing Wang
- Lu Zhang
