  The emergence of novel pathogens and zoonotic diseases like the SARS-CoV-2
have underlined the need for developing novel diagnosis and intervention
pipelines that can learn rapidly from small amounts of labeled data. Combined
with technological advances in next-generation sequencing, metagenome-based
diagnostic tools hold much promise to revolutionize rapid point-of-care
diagnosis. However, there are significant challenges in developing such an
approach, the chief among which is to learn self-supervised representations
that can help detect novel pathogen signatures with very low amounts of labeled
data. This is particularly a difficult task given that closely related
pathogens can share more than 90% of their genome structure. In this work, we
address these challenges by proposing MG-Net, a self-supervised representation
learning framework that leverages multi-modal context using pseudo-imaging data
derived from clinical metagenome sequences. We show that the proposed framework
can learn robust representations from unlabeled data that can be used for
downstream tasks such as metagenome sequence classification with limited access
to labeled data. Extensive experiments show that the learned features
outperform current baseline metagenome representations, given only 1000 samples
per class.
