Idioma:

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

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

Frontiers in bioengineering and biotechnology, 2018-04, Vol.6, p.31-31 [Periódico revisado por pares]

Switzerland: Frontiers Media S.A

Texto completo disponível

Citações Citado por

Enviar para

Título:
A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus
Autor: 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
Assuntos: Bioengineering and Biotechnology ; diseases diagnosis ; high resolution mass spectrometry ; machine learning ; random forest ; Zika diagnosis ; Zika virus
É parte de: Frontiers in bioengineering and biotechnology, 2018-04, Vol.6, p.31-31
Notas: ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Reviewed by: Hailun Wang, University of Hong Kong, Hong Kong; Evagelia C. Laiakis, Georgetown University, United States
Specialty section: This article was submitted to Bioinformatics and Computational Biology, a section of the journal Frontiers in Bioengineering and Biotechnology
Edited by: Herbert Pang, University of Hong Kong, Hong Kong
Descrição: 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.
Editor: Switzerland: Frontiers Media S.A
Idioma: Inglês

Links

View this record in MEDLINE/PubMed

Voltar para lista de resultados

Anterior Resultado 3 Avançar Ir para próxima página

Realização: Logos de Redes Sociais:

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

Switzerland: Frontiers Media S.A

Buscando em bases de dados remotas. Favor aguardar.