AUTHOR=Melo Carlos Fernando Odir Rodrigues , Navarro Luiz Claudio , de Oliveira Diogo Noin , Guerreiro Tatiane Melina , Lima Estela de Oliveira , Delafiori Jeany , Dabaja Mohamed Ziad , Ribeiro Marta da Silva , de Menezes Maico , 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 , Costa Fábio Trindade Maranhão , Arns Clarice Weis , Resende Mariangela Ribeiro , Amaral Eliana , Junior Renato Passini , Ribeiro-do-Valle Carolina C. , Milanez Helaine , Moretti Maria Luiza , Proenca-Modena Jose Luiz , Avila Sandra , Rocha Anderson , Catharino Rodrigo Ramos 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 JOURNAL=Frontiers in Bioengineering and Biotechnology VOLUME=6 YEAR=2018 URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2018.00031 DOI=10.3389/fbioe.2018.00031 ISSN=2296-4185 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, nonetheless, 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 spectrometric data that are inputted with 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.