Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutio...

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Bibliographic Details
Main Authors: Agbavor, F. (Author), Mell, J.C (Author), Rosen, G.L (Author), Sokhansanj, B.A (Author), Woloszynek, S. (Author), Zhao, Z. (Author)
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
algorithm
Algorithms
amplicon
Article
attention network
biology
classification
Computational Biology
Databases, Genetic
Deep Learning
deep neural network
Gastrointestinal Microbiome
genetic database
genetic marker
genetics
Host Microbial Interactions
human
Humans
inflammatory bowel disease
Inflammatory Bowel Diseases
intestine flora
isolation and purification
microbial community
microbiology
Microbiota
microflora
natural language processing
Natural Language Processing
Neural Networks, Computer
nonhuman
phenotype
Phenotype
polymerase chain reaction
prediction
Prevotella
proof of concept
Proof of Concept Study
RNA 16S
RNA gene
RNA structure
RNA, Ribosomal, 16S
taxonomy
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