GRaDL: A Framework for Animal Genome Sequence Classification with Graph Representations and Deep Learning

Narayanan, Sai; Ramachandran, Akhilesh; Aakur, Sathyanarayanan N.; Bagavathi, Arunkumar

doi:10.1109/icmla51294.2020.00203

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…As expected, the performance gets better with the use of increasing amounts of labeled data. It is interesting to note that we match the performance of fully supervised models like sequence-level graph-based representations [23] and end-to-end deep learning models like Seq2Vec with as little as 500 samples and outperform their performance with as little as 1000 labeled samples per class. Given the performance of the linear classifier (Table 2), we can see that our approach learns robust representations with limited data.…”

Section: Quantitative Evaluationmentioning

confidence: 58%

“…First, we construct a global graph representation of the entire metagenome sample, i.e., the graph provides a structural representation of the sequenced clinical sample. We take inspiration from the success of De Bruijn graphs for genome analysis [20,23] and use a modified version to represent the metagenome sample. Given a metagenome sample X with sequence reads X 0 , X 1 , .…”

Section: Capturing the Global Structure With Graph Representationsmentioning

confidence: 99%

“…Precision and recall, on the other hand, allow us to quantify the false alarm rates and provide more precise detection accuracy. We compare against other representation learning frameworks for metagenome analysis proposed, such as graph-based approaches [23], and a deep learning model termed Seq2Vec, an endto-end sequence-based learning model based on DeePAC [4]. For classification, we consider both traditional baselines such as logistic regression (LR), support vector machines (SVM), and multi-layer perceptron (MLP), as well as a deep neural network (DL).…”

Section: Experimental Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

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

Aakur¹,

Narayanan²,

Indla³

et al. 2021

Preprint

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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.

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Section: Quantitative Evaluationmentioning

confidence: 58%

Section: Capturing the Global Structure With Graph Representationsmentioning

confidence: 99%