ArticleFrontiers in bioengineering and biotechnology2018
A Machine Learning Application Based in Random Forest for Integrating Mass Spectrometry-Based Metabolomic Data: A Simple Screening Method for Patients With Zika Virus.
Article in Frontiers in bioengineering and biotechnology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Temporal and Spatiotemporal Arboviruses Forecasting by Machine Learning: A Systematic Review.Frontiers in public health · 2022Pooled it
- Machine Learning-Driven Metabolomic Biomarker Discovery in Glioblastoma: Advances, Challenges, and Future Directions.International journal of molecular sciences · 2026Review
- Explainable Machine Learning Integrating Patient and Environmental Factors for Predicting Multidrug-Resistant Organism Colonization or Infection on ICU Admission.Infection and drug resistance · 2026Article
- Artificial Intelligence in Metabolomics: A Current Review.Trends in analytical chemistry : TRAC · 2024Article
- Precision Medicine Approaches with Metabolomics and Artificial Intelligence.International journal of molecular sciences · 2022Review
- Article
- Assessment of the Risk of Severe Dengue Using Intrahost Viral Population in Dengue Virus Serotype 2 PatientsFrontiers in cellular and infection microbiology · 2022Article
- Untargeted Metabolomics Insights into Newborns with Congenital Zika Infection.Pathogens (Basel, Switzerland) · 2021Article
- Metabolomics and Machine Learning Approaches Combined in Pursuit for More Accurate Paracoccidioidomycosis Diagnoses.mSystems · 2020Article
- MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery.Metabolites · 2020Article
- Reducing False-Positive Results in Newborn Screening Using Machine Learning.International journal of neonatal screening · 2020Article
- Severity Index for Suspected Arbovirus (SISA): Machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection.PLoS neglected tropical diseases · 2020Article
- Combining Machine Learning and Metabolomics to Identify Weight Gain Biomarkers.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
31 authors.
Funding
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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.
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