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Title: A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus
Author: Melo, Carlos Fernando Odir Rodrigues; Rodrigues, Rafael Gustavo Martins; Morishita, Karen Noda; Esteves, Cibele Zanardi; De Amorim, Aline Lopes Lucas; Aoyagui, Caroline Tiemi; Parise, Pierina Lorencini; Milanez, Guilherme Paier; Do Nascimento, Gabriela Mansano; Ribas Freitas, André Ricardo; Angerami, Rodrigo; Navarro, Luiz Claudio; Proença-módena, J. L.; De Oliveira, Diogo Noin; Guerreiro, Tatiane Melina; Lima, Estela De Oliveira; Delafiori, Jeany; Dabaja, Mohamed Ziad; Ribeiro, Marta Da Silva; De Menezes, Maico
Year: 2018
Is part of: FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, v. 6, p. 31 -
DOI: https://doi.org/10.3389/fbioe.2018.00031

Citation: Melo, Carlos Fernando Odir Rodrigues; Rodrigues, Rafael Gustavo Martins; Morishita, Karen Noda; Esteves, Cibele Zanardi; De Amorim, Aline Lopes Lucas; Aoyagui, Caroline Tiemi; Parise, Pierina Lorencini; Milanez, Guilherme Paier; Do Nascimento, Gabriela Mansano; Ribas Freitas, André Ricardo; Angerami, Rodrigo; Navarro, Luiz Claudio; Proença-módena, J. L.; De Oliveira, Diogo Noin; Guerreiro, Tatiane Melina; Lima, Estela De Oliveira; Delafiori, Jeany; Dabaja, Mohamed Ziad; Ribeiro, Marta Da Silva; De Menezes, Maico; A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus. FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY, v.6, p. 31-, 2018

Abstract: Recent Zika outbreaks in South America, accompanied by unexpectedly severe clinical complications have brought much interest in fast and reliable screening methods for ZIKV (Zika virus) identification. Reverse-transcriptase polymerase chain reaction (RT-PCR) is currently the method of choice to detect ZIKV in biological samples. This approach, none-theless, demands a considerable amount of time and resources such as kits and reagents that, in endemic areas, may result in a substantial financial burden over affected individuals and health services veering away from RT-PCR analysis. This study presents a powerful combination of high-resolution mass spectrometry and a machine-learning prediction model for data analysis to assess the existence of ZIKV infection across a series of patients that bear similar symptomatic conditions, but not necessarily are infected with the disease. By using mass spectrometry data that are inputted wrth the developed decision-making algorithm, we were able to provide a set of features that work as a "fingerprint" for this specific pathophysiological condition, even after the acute phase of infection. Since both mass spectrometry and machine learning approaches are well-established and have largely utilized tools within their respective fields, this combination of methods emerges as a distinct alternative for clinical applications, providing a diagnostic screening-faster and more accurate-with improved cost-effectiveness when compared to existing technologies.



Funding: The authors would like to thank the Sao Paulo Research Foundation (FAPESP) for the fellowships of CM (16/17066-2), CE (14/00302-0), and AR (2017/12646-3), and the grants for RC (11/50400-0, 15/06809-1, and 17/20614-4). We also acknowledge CAPES for the scholarships for EL (PNPD 1578388) and TG (PROEX 1489740). DO acknowledges the Plano Nacional de Enfrentamento ao Aedes aegypti e Microcefalia from the Brazilian Ministry of Health for the fellowship (88887.137889/2017-00). AR thanks CNPq for his grant No. 304472/2015-8.
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