A recent research article in partnership of Recodists Prof. Anderson Rocha, Prof. Sandra Avila and Luiz Navarro and researchers at the Faculty of Pharmacy led by Prof. Rodrigo Catharino published at Frontiers in Bioengineering and Biotechnology journal is getting the media attention. The article, entitled “A machine learning application based in random forest for integrating mass spectrometry-based metabolomic data: a simple screening method for patients with zika virus”, presents a powerful solution against the analysis of the Zika virus presence based on high-resolution mass spectrometry data and machine-learning prediction model.
Since both mass spectrometry and machine learning approaches are well-established and 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.
