Cargando…
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...
Autores principales: | Zhao, Zhengqiao, Woloszynek, Stephen, Agbavor, Felix, Mell, Joshua Chang, Sokhansanj, Bahrad A., Rosen, Gail L. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8496832/ https://www.ncbi.nlm.nih.gov/pubmed/34550967 http://dx.doi.org/10.1371/journal.pcbi.1009345 |
Ejemplares similares
-
Exploring thematic structure and predicted functionality of 16S rRNA amplicon data
por: Woloszynek, Stephen, et al.
Publicado: (2019) -
Interpretable and Predictive Deep Neural Network Modeling of the SARS-CoV-2 Spike Protein Sequence to Predict COVID-19 Disease Severity
por: Sokhansanj, Bahrad A., et al.
Publicado: (2022) -
16S rRNA sequence embeddings: Meaningful numeric feature representations of nucleotide sequences that are convenient for downstream analyses
por: Woloszynek, Stephen, et al.
Publicado: (2019) -
Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization
por: Zhao, Zhengqiao, et al.
Publicado: (2020) -
Amino Acid k-mer Feature Extraction for Quantitative Antimicrobial Resistance (AMR) Prediction by Machine Learning and Model Interpretation for Biological Insights
por: ValizadehAslani, Taha, et al.
Publicado: (2020)