# Scalable Pathogen Detection from Next Generation DNA Sequencing with Deep Learning

arXiv: https://arxiv.org/abs/2212.00015 · HTML: https://mlsanity.com/paper/2212.00015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 90 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [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)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering](https://mlsanity.com/paper/1809.02789/) (2018)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [CutPaste: Self-Supervised Learning for Anomaly Detection and Localization](https://mlsanity.com/paper/2104.04015/) (2021)
- [A Perceptual Prediction Framework for Self Supervised Event Segmentation](https://mlsanity.com/paper/1811.04869/) (2018)
- [GEFA: Early Fusion Approach in Drug-Target Affinity Prediction](https://mlsanity.com/paper/2009.12146/) (2020)
- [Metagenome2Vec: Building Contextualized Representations for Scalable Metagenome Analysis](https://mlsanity.com/paper/2111.08001/) (2021)
- [GeNet: Deep Representations for Metagenomics](https://mlsanity.com/paper/1901.11015/) (2019)
- [MG-NET: Leveraging Pseudo-Imaging for Multi-Modal Metagenome Analysis](https://mlsanity.com/paper/2107.09883/) (2021)
- [Large-scale Machine Learning for Metagenomics Sequence Classification](https://mlsanity.com/paper/1505.06915/) (2015)
- [Genome Sequence Classification for Animal Diagnostics with Graph Representations and Deep Neural Networks](https://mlsanity.com/paper/2007.12791/) (2020)

## Abstract (arXiv)

Next-generation sequencing technologies have enhanced the scope of Internet-of-Things (IoT) to include genomics for personalized medicine through the increased availability of an abundance of genome data collected from heterogeneous sources at a reduced cost. Given the sheer magnitude of the collected data and the significant challenges offered by the presence of highly similar genomic structure across species, there is a need for robust, scalable analysis platforms to extract actionable knowledge such as the presence of potentially zoonotic pathogens. The emergence of zoonotic diseases from novel pathogens, such as the influenza virus in 1918 and SARS-CoV-2 in 2019 that can jump species barriers and lead to pandemic underscores the need for scalable metagenome analysis. In this work, we propose MG2Vec, a deep learning-based solution that uses the transformer network as its backbone, to learn robust features from raw metagenome sequences for downstream biomedical tasks such as targeted and generalized pathogen detection. Extensive experiments on four increasingly challenging, yet realistic diagnostic settings, show that the proposed approach can help detect pathogens from uncurated, real-world clinical samples with minimal human supervision in the form of labels. Further, we demonstrate that the learned representations can generalize to completely unrelated pathogens across diseases and species for large-scale metagenome analysis. We provide a comprehensive evaluation of a novel representation learning framework for metagenome-based disease diagnostics with deep learning and provide a way forward for extracting and using robust vector representations from low-cost next generation sequencing to develop generalizable diagnostic tools.