# Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis

arXiv: https://arxiv.org/abs/2111.08001 · HTML: https://mlsanity.com/paper/2111.08001/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 9 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Jointly embedding the local and global relations of heterogeneous graph for rumor detection](https://mlsanity.com/paper/1909.04465/) (2019)
- [GeNet: Deep Representations for Metagenomics](https://mlsanity.com/paper/1901.11015/) (2019)
- [Phylogenetic Convolutional Neural Networks in Metagenomics](https://mlsanity.com/paper/1709.02268/) (2017)
- [Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks](https://mlsanity.com/paper/2007.12791/) (2020)
- [Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks](https://mlsanity.com/paper/1712.00244/) (2017)

## Built on by (most cited first, 4 of 4)

- [ProtoKD: Learning from Extremely Scarce Data for Parasite Ova Recognition](https://mlsanity.com/paper/2309.10210/) (2023)
- [TEPI: Taxonomy-aware Embedding and Pseudo-Imaging for Scarcely-labeled Zero-shot Genome Classification](https://mlsanity.com/paper/2401.13219/) (2024)
- [Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning](https://mlsanity.com/paper/2212.00015/) (2022)
- [Learning Genomic Sequence Representations using Graph Neural Networks over De Bruijn Graphs](https://mlsanity.com/paper/2312.03865/) (2023)

## Abstract (arXiv)

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.