Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis
This addresses the need for scalable diagnosis of novel pathogen infections in clinical settings, though it appears incremental as it builds on self-supervised learning for a specific domain.
The paper tackled the problem of scalable metagenome analysis for pathogen detection by proposing Metagenome2Vec, a contextualized representation method, which enabled detection of six related pathogens with less than 100 labeled sequences.
Advances in next-generation metagenome sequencing have the potential to revolutionize the point-of-care diagnosis of novel pathogen infections, which could help prevent potential widespread transmission of diseases. Given the high volume of metagenome sequences, there is a need for scalable frameworks to analyze and segment metagenome sequences from clinical samples, which can be highly imbalanced. There is an increased need for learning robust representations from metagenome reads since pathogens within a family can have highly similar genome structures (some more than 90%) and hence enable the segmentation and identification of novel pathogen sequences with limited labeled data. In this work, we propose Metagenome2Vec - a contextualized representation that captures the global structural properties inherent in metagenome data and local contextualized properties through self-supervised representation learning. We show that the learned representations can help detect six (6) related pathogens from clinical samples with less than 100 labeled sequences. Extensive experiments on simulated and clinical metagenome data show that the proposed representation encodes compositional properties that can generalize beyond annotations to segment novel pathogens in an unsupervised setting.