Abstract
Machine learning models have made great strides in many fields of research, including bioinformatics and animal diagnostics. Recently, attention has shifted to detecting pathogens from genome sequences for disease diagnostics with computational models. While there has been tremendous progress, it has primarily been driven by large amounts of annotated data, which is expensive and hard to obtain. Hence, there is a need to develop models that can leverage low-cost, synthetic genome sequences to help tackle complex metagenome classification problems for diagnostics. In this paper, we present one of the first sim2real approaches to help multi-task deep learning models learn robust feature representations from synthetic metagenome sequences and transfer the learning to predict pathogen sequences in real data. Extensive experiments show that our model can successfully leverage synthetic and real genome sequences to obtain \(80\%\) accuracy on metagenome sequence classification. Additionally, we show that our proposed model obtains 76% accuracy, with limited real metagenome sequences.
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Acknowledgement
This research was supported in part by the US Department of Agriculture (USDA) grants AP20VSD and B000C011.
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Indla, V. et al. (2021). Sim2Real for Metagenomes: Accelerating Animal Diagnostics with Adversarial Co-training. In: Karlapalem, K., et al. Advances in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12712. Springer, Cham. https://doi.org/10.1007/978-3-030-75762-5_14
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DOI: https://doi.org/10.1007/978-3-030-75762-5_14
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