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Article
Open AccessAdversarial and variational autoencoders improve metagenomic binning
Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grou** the sequences by their ...
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Article
Open AccessAuthor Correction: Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
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Article
Open AccessDiscovery of drug–omics associations in type 2 diabetes with generative deep-learning models
The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogen...
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Article
Open AccessGenome binning of viral entities from bulk metagenomics data
Despite the accelerating number of uncultivated virus sequences discovered in metagenomics and their apparent importance for health and disease, the human gut virome and its interactions with bacteria in the g...
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Article
Improved metagenome binning and assembly using deep variational autoencoders
Despite recent advances in metagenomic binning, reconstruction of microbial species from metagenomics data remains challenging. Here we develop variational autoencoders for metagenomic binning (VAMB), a progra...
