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SpeciesMLP: Sequence based Multi-layer Perceptron for Amplicon Read Classification Using Real-time Data Augmentation
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Biopython: freely available Python tools for computational molecular biology and bioinformatics
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BLAST: at the core of a powerful and diverse set of sequence analysis tools
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The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
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MicroPheno: Predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples
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Multi-Layer and Recursive Neural Networks for Metagenomic Classification
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One-shot learning of object categories
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Characterization of novel antibiotic resistance genes identified by functional metagenomics on soil samples
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Naive Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy
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Response of Microbial Community Function to Fluctuating Geochemical Conditions within a Legacy Radioactive Waste Trench Environment
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Greengenes, a Chimera-Checked 16S rRNA Gene Database and Workbench Compatible with ARB
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A unified architecture for natural language processing
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Automated generation of heuristics for biological sequence comparison
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A Bayesian taxonomic classification method for 16S rRNA gene sequences with improved species-level accuracy
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Phylogenetic convolutional neural networks in metagenomics
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Metagenomic cross-talk: the regulatory interplay between immunogenomics and the microbiome
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Whole metagenome profiles of particulates collected from the International Space Station
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DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data
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16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets
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Convolutional Neural Networks for Sentence Classification
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Gated Feedback Recurrent Neural Networks
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Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
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Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks
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