Journal Article

A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus

Esteves, Cibele Zanardi, Delafiori, Jeany, Junior, Renato Passini, Parise, Pierina Lorencini, Arns, Clarice Weis, Milanez, Helaine, Proenca-Modena, Jose Luiz, do Nascimento, Gabriela Mansano, Morishita, Karen Noda, Milanez, Guilherme Paier, de Amorim, Aline Lopes Lucas, Moretti, Maria Luiza, Angerami, Rodrigo, Ribas Freitas, André Ricardo, Rocha, Anderson, Dabaja, Mohamed Ziad, Resende, Mariangela Ribeiro, Lima, Estela de Oliveira, Aoyagui, Caroline Tiemi, Melo, Carlos Fernando Odir Rodrigues, Ribeiro-do-Valle, Carolina C., Navarro, Luiz Claudio, Avila, Sandra, de Oliveira, Diogo Noin, Guerreiro, Tatiane Melina, Costa, Fábio Trindade Maranhão, de Menezes, Maico, Catharino, Rodrigo Ramos, Ribeiro, Marta da Silva, Amaral, Eliana, Rodrigues, Rafael Gustavo Martins

2018

Overview

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.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Accuracy / Algorithms / Bioengineering and Biotechnology / Classification / Data processing / Decision making / Decision trees / diseases diagnosis / high resolution mass spectrometry / Learning algorithms / Machine learning / Mass spectrometry / Mass spectroscopy / Medical screening / Metabolomics / Microcephaly / Patients / Pest outbreaks / Polymerase chain reaction / Prediction models / random forest / Scientific imaging / Viral infections / Zika diagnosis / Zika virus