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

Gomes, Juliana Carneiro; Silva, Leandro Honorato de S.; Ferreira, Janderson; Júnior, Agostinho A. F.; Rocha, Allana Lais dos Santos; Castro, Letícia; Silva, Nathália R. C. da; Fernandes, Bruno; Santos, Wellington Pinheiro dos

doi:10.1101/2020.06.02.129775

Cited by 8 publications

(5 citation statements)

References 43 publications

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“…Improving DNA tests: The mainstream in diagnosing the disease is DNA identification of the virus. In order to improve the process of DNA identification, a pseudo-convolutional machine learning is proposed in [87] , which divides the DNA sequence into smaller sequences with overlap. The method uses co-occurrence matrices and analyses DNA sequences obtained by the benchmark RT-PCR method which eliminates sequence alignment.…”

Section: Clinical Applicationsmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

161

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Section: Clinical Applicationsmentioning

confidence: 99%

Applications of artificial intelligence in battling against covid-19: A literature review

2021

Chaos, Solitons & Fractals

161

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show abstract

“…In this sense, many other studies are also being conducted in order to optimize Covid-19 diagnosis using these other testing methods. Gomes et al ( 2020c ) proposed a new technique for representing DNA sequences to optimize the molecular diagnosis of Covid-19. Their method divides the DNA sequences into smaller sequences with overlap in a pseudo-convolutional approach, and represented by co-occurrence matrices.…”

Section: Related Workmentioning

confidence: 99%

Covid-19 rapid test by combining a Random Forest-based web system and blood tests

Barbosa

Gomes

Santana

et al. 2021

Journal of Biomolecular Structure and Dynamics

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The disease caused by the new type of coronavirus, Covid-19, has posed major public health challenges for many countries. With its rapid spread, since the beginning of the outbreak in December 2019, the disease transmitted by SARS-CoV-2 has already caused over 2 million deaths to date. In this work, we propose a web solution, called Heg.IA, to optimize the diagnosis of Covid-19 through the use of artificial intelligence. Our system aims to support decision-making regarding to diagnosis of Covid-19 and to the indication of hospitalization on regular ward, semi-ICU or ICU based on decision a Random Forest architecture with 90 trees. The main idea is that healthcare professionals can insert 41 hematological parameters from common blood tests and arterial gasometry into the system. Then, Heg.IA will provide a diagnostic report. The system reached good results for both Covid-19 diagnosis and to recommend hospitalization. For the first scenario we found average results of accuracy of 92.891%±0.851, kappa index of 0.858 ± 0.017, sensitivity of 0.936 ± 0.011, precision of 0.923 ± 0.011, specificity of 0.921 ± 0.012 and area under ROC of 0.984 ± 0.003. As for the indication of hospitalization, we achieved excellent performance of accuracies above 99% and more than 0.99 for the other metrics in all situations. By using a computationally simple method, based on the classical decision trees, we were able to achieve high diagnosis performance. Heg.IA system may be a way to overcome the testing unavailability in the context of Covid-19. Communicated by Ramaswamy H. Sarma

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“…The standard diagnostic approach for COVID-19 is the real-time reverse-transcriptase polymerase chain reaction (rRT-PCR) technique with DNA sequencing and identification. Gomes et al (32) proposed a pseudoconvolutional machine learning method to improve the process of DNA identification by dividing the DNA sequence into more minor sequences with overlap. Then it optimized the COVID-19 molecular diagnosis to identify SARS-Cov-2 DNA sequences faster with higher specificity and sensitivity by different models, such as random forests (RF), naive Bayes classifier (NBC), instance-based learner (IBL), multilayer perceptron (MLP), support vector machine (SVM).…”

Section: Laboratory-based Diagnosismentioning

confidence: 99%

Application of artificial intelligence in COVID-19 medical area: a systematic review

Chang¹,

Zhan²,

Zhao³

et al. 2021

J Thorac Dis

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Background: Coronavirus disease 2019 has caused a large-scale global epidemic, impacting international politics and the economy. At present, there is no particularly effective medicine and treatment plan. Therefore, it is urgent and significant to find new technologies to diagnose early, isolate early, and treat early. Multimodal data drove artificial intelligence (AI) can potentially be the option. During the COVID-19 Pandemic, AI provided cutting-edge applications in disease, medicine, treatment, and target recognition.This paper reviewed the literature on the intersection of AI and medicine to analyze and compare different AI model applications in the COVID-19 Pandemic, evaluate their effectiveness, show their advantages and differences, and introduce the main models and their characteristics. Methods: We searched PubMed, arXiv, medRxiv, and Google Scholar through February 2020 to identify studies on AI applications in the medical areas for the COVID-19 Pandemic. Results: We summarize the main AI applications in six areas: (I) epidemiology, (II) diagnosis, (III) progression, (IV) treatment, (V) psychological health impact, and (VI) data security. The ongoing development in AI has significantly improved prediction, contact tracing, screening, diagnosis, treatment, medication, and vaccine development for the COVID-19 Pandemic and reducing human intervention in medical practice. Discussion: This paper provides strong advice for using AI-based auxiliary tools for related applications of human diseases. We also discuss the clinicians' role in the further development of AI. They and AI researchers can integrate AI technology with current clinical processes and information systems into applications. In the future, AI personnel and medical workers will further cooperate closely.

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

Cited by 8 publications

References 43 publications

Applications of artificial intelligence in battling against covid-19: A literature review

Applications of artificial intelligence in battling against covid-19: A literature review

Covid-19 rapid test by combining a Random Forest-based web system and blood tests

Application of artificial intelligence in COVID-19 medical area: a systematic review

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