Growing Self-Organizing Maps for Metagenomic Visualizations Supporting Disease Classification

Nguyễn, Hải Thanh; Nguyen, Bang Anh; Nguyen, My N.; Truong, Quoc Dinh; Nguyen, L.; Banh, Thao Thuy Ngoc; Linh, Phung Duong

doi:10.1007/978-3-030-63924-2_9

Cited by 3 publications

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, there have been very few methods used for metagenome analysis. For example, Self-Organizing Maps (SOM) [26] and Growing Self-Organizing Maps (GSOM) [25] have been used to represent metagenome sequences as images and a shallow CNN model was used for disease prediction. A matrix representation of a polygenetic tree has been used with CNN to predict host phenotype of metagenome sequences [28].…”

Section: Introductionmentioning

confidence: 99%

MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis

Aakur¹,

Narayanan²,

Indla³

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%