About: Optimizing the molecular diagnosis of Covid-19 by combining RT-PCR and a pseudo-convolutional machine learning approach to characterize virus DNA sequences

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About: Optimizing the molecular diagnosis of Covid-19 by combining RT-PCR and a pseudo-convolutional machine learning approach to characterize virus DNA sequences Goto Sponge NotDistinct Permalink

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Optimizing the molecular diagnosis of Covid-19 by combining RT-PCR and a pseudo-convolutional machine learning approach to characterize virus DNA sequences
Creator	Santos, Dos Silva, S Gomes, Juliana Pinheiro, Wellington Castro, Letícia Da Silva, Nathália Fernandes, Bruno Ferreira, Janderson Honorato, Leandro Júnior, Agostinho Lais, Allana Rocha, Santos
source	BioRxiv
abstract	The proliferation of the SARS-Cov-2 virus to the whole world caused more than 250,000 deaths worldwide and over 4 million confirmed cases. The severity of Covid-19, the exponential rate at which the virus proliferates, and the rapid exhaustion of the public health resources are critical factors. The RT-PCR with virus DNA identification is still the benchmark Covid-19 diagnosis method. In this work we propose a new technique for representing DNA sequences: they are divided into smaller sequences with overlap in a pseudo-convolutional approach, and represented by co-occurrence matrices. This technique analyzes the DNA sequences obtained by the RT-PCR method, eliminating sequence alignment. Through the proposed method, it is possible to identify virus sequences from a large database: 347,363 virus DNA sequences from 24 virus families and SARS-Cov-2. Experiments with all 24 virus families and SARS-Cov-2 (multi-class scenario) resulted 0.822222 ± 0.05613 for sensitivity and 0.99974 ± 0.00001 for specificity using Random Forests with 100 trees and 30% overlap. When we compared SARS-Cov-2 with similar-symptoms virus families, we got 0.97059 ± 0.03387 for sensitivity, and 0.99187 ± 0.00046 for specificity with MLP classifier and 30% overlap. In the real test scenario, in which SARS-Cov-2 is compared to Coronaviridae and healthy human DNA sequences, we got 0.98824 ± 001198 for sensitivity and 0.99860 ± 0.00020 for specificity with MLP and 50% overlap. Therefore, the molecular diagnosis of Covid-19 can be optimized by combining RT-PCR and our pseudo-convolutional method to identify SARS-Cov-2 DNA sequences faster with higher specificity and sensitivity.
has issue date	2020-06-02(xsd:dateTime)
bibo:doi	10.1101/2020.06.02.129775
has license	biorxiv
sha1sum (hex)	393d5f4f23d04446df46a35cc5e5991c8c06a484
schema:url	https://doi.org/10.1101/2020.06.02.129775
resource representing a document's title	Optimizing the molecular diagnosis of Covid-19 by combining RT-PCR and a pseudo-convolutional machine learning approach to characterize virus DNA sequences
schema:publication	bioRxiv
resource representing a document's body	covid:393d5f4f23d04446df46a35cc5e5991c8c06a484#body_text
is schema:about of	named entity 'cases' named entity 'SMALLER' named entity 'SEVERITY' named entity 'MORE THAN' named entity 'CRITICAL' named entity 'COVID-19 DIAGNOSIS' named entity 'CAUSED' named entity 'RT-PCR' named entity 'proliferation' named entity 'sequences' named entity 'RT-PCR' named entity 'exponential rate' named entity 'co-occurrence matrices' named entity 'public health' named entity 'Covid-19' named entity 'Covid-19' named entity 'Covid' named entity 'DNA sequences' named entity 'virus' named entity 'Covid' named entity 'virus' named entity 'DNA sequences' named entity 'RT-PCR' named entity 'virus' named entity 'virus' named entity 'DNA sequences' named entity 'sensitivity and specificity' named entity 'machine learning' named entity 'rapid diagnostic' named entity 'sensitivity, specificity' named entity 'human genome' named entity '0.9' named entity 'feature extraction' named entity 'exponential rate' named entity 'DNA identification' named entity 'feature extraction' named entity 'True Positive Rate' named entity '0.98' named entity 'precision, recall' named entity 'neuron' named entity 'Kappa Coefficient' named entity 'virus' named entity 'confusion matrices' named entity 'sneezing' named entity 'WHO' named entity 'Coronaviridae' named entity 'cDNA' named entity 'TNR' named entity 'confusion matrix' named entity 'genome sequence' named entity 'vectorial representation' named entity 'DNA sequences' named entity 'Hantaviridae' named entity 'genetic code' named entity 'probability' named entity 'China' named entity 'binary classification' named entity 'SARS' named entity 'NPV' named entity 'SARS' named entity 'computational complexity' named entity 'Covid-19' named entity 'machine learning' named entity 'SARS-Cov' named entity 'co-occurrence matrix' named entity 'confusion matrix' named entity 'genome sequence' named entity 'nitrogenous bases' named entity 'RT-PCR' named entity 'virus' named entity 'creatinine' named entity 'IgG' named entity 'Covid'

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