Taxonomic classification of metagenomic sequences from Relative Abundance Index profiles using deep learning

2021 ◽  
Vol 67 ◽  
pp. 102539
Author(s):  
Meryem Altın Karagöz ◽  
O. Ufuk Nalbantoglu
Keyword(s):  
Deep Learning ◽  
Relative Abundance ◽  
Taxonomic Classification ◽  
Abundance Index

scholarly journals Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

2021 ◽  
Author(s):  
Gracielly G. F. Coutinho ◽  
Gabriel B. M. Câmara ◽  
Raquel de M. Barbosa ◽  
Marcelo A. C. Fernandes
Keyword(s):  
Deep Learning ◽  
Viral Genome ◽  
Genomic Sequence ◽  
Confusion Matrix ◽  
Taxonomic Classification ◽  
Classification Problems ◽  
Virus Identification ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on the stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k=6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.

scholarly journals Deep learning models for bacteria taxonomic classification of metagenomic data

2018 ◽  
Vol 19 (S7) ◽  
Author(s):  
Antonino Fiannaca ◽  
Laura La Paglia ◽  
Massimo La Rosa ◽  
Giosue’ Lo Bosco ◽  
Giovanni Renda ◽  
...  
Keyword(s):  
Deep Learning ◽  
Taxonomic Classification ◽  
Metagenomic Data ◽  
Learning Models

Classification of Customer to Upgrade Profits in Retail Market with Deep Learning Methodology

2019 ◽  
Vol 7 (5) ◽  
pp. 188-191
Author(s):  
I.Gayathri Devi ◽  
G. Surya Kala Eswari ◽  
G. Kumari
Keyword(s):  
Deep Learning ◽  
Retail Market
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