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...
Guardado en:
Autores principales: | Zhengqiao Zhao, Stephen Woloszynek, Felix Agbavor, Joshua Chang Mell, Bahrad A Sokhansanj, Gail L Rosen |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Public Library of Science (PLoS)
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f942b69fd3b3444fabf60265b9f20db6 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
The pervasive effects of an antibiotic on the human gut microbiota, as revealed by deep 16S rRNA sequencing.
por: Les Dethlefsen, et al.
Publicado: (2008) -
Sputum microbiota in tuberculosis as revealed by 16S rRNA pyrosequencing.
por: Man Kit Cheung, et al.
Publicado: (2013) -
Toward an Understanding of Changes in Diversity Associated with Fecal Microbiome Transplantation Based on 16S rRNA Gene Deep Sequencing
por: Dea Shahinas, et al.
Publicado: (2012) -
Bacterial Diversity of Intestinal Microbiota in Patients with Substance Use Disorders Revealed by 16S rRNA Gene Deep Sequencing
por: Yu Xu, et al.
Publicado: (2017) -
16S rRNA Amplicon Sequencing for Epidemiological Surveys of Bacteria in Wildlife
por: Maxime Galan, et al.
Publicado: (2016)