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• W3128792544 abstract "Abstract Viruses evolve extremely quickly, so reliable methods for viral host prediction are necessary to safeguard biosecurity and biosafety alike. Novel human-infecting viruses are difficult to detect with standard bioinformatics workflows. Here, we predict whether a virus can infect humans directly from next-generation sequencing reads. We show that deep neural architectures significantly outperform both shallow machine learning and standard, homology-based algorithms, cutting the error rates in half and generalizing to taxonomic units distant from those presented during training. Further, we develop a suite of interpretability tools and show that it can be applied also to other models beyond the host prediction task. We propose a new approach for convolutional filter visualization to disentangle the information content of each nucleotide from its contribution to the final classification decision. Nucleotide-resolution maps of the learned associations between pathogen genomes and the infectious phenotype can be used to detect regions of interest in novel agents, for example, the SARS-CoV-2 coronavirus, unknown before it caused a COVID-19 pandemic in 2020. All methods presented here are implemented as easy-to-install packages not only enabling analysis of NGS datasets without requiring any deep learning skills, but also allowing advanced users to easily train and explain new models for genomics." @default.
• W3128792544 created "2021-02-15" @default.
• W3128792544 creator A5005996110 @default.
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• W3128792544 date "2021-02-01" @default.
• W3128792544 modified "2023-10-16" @default.
• W3128792544 title "Interpretable detection of novel human viruses from genome sequencing data" @default.
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• W3128792544 doi "https://doi.org/10.1093/nargab/lqab004" @default.
• W3128792544 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7849996" @default.
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• W3128792544 hasPublicationYear "2021" @default.
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