Advances in clinical metagenomics: predictive capability of the salivary microbiome using machine learning for the diagnosis of oral and systemic diseases
The hypothesis is to demonstrate the predictive capacity of the salivary microbiome to discriminate with high diagnostic accuracy the presence of oral diseases and systemic diseases. For this purpose, starting from the data obtained from massive sequencing of the 16S rRNA gene on salivary samples, we propose the development of predictive models that will allow the classification of these pathologies.
Objectives
From the data obtained by massive sequencing of the 16S rRNA gene on salivary samples and applying machine learning, the objective is the development of predictive models that will allow us to classify oral or systemic pathologies from a single sample per patient.
Project
/research/projects/avances-en-metagenomica-clinica-capacidad-predictiva-del-microbioma-salival-mediante-machine-learning-para-el-diagnostico-de-enfermedades-orales-y-sistemicas
<p>The hypothesis is to demonstrate the predictive capacity of the salivary microbiome to discriminate with high diagnostic accuracy the presence of oral diseases and systemic diseases. For this purpose, starting from the data obtained from massive sequencing of the 16S rRNA gene on salivary samples, we propose the development of predictive models that will allow the classification of these pathologies.</p><p>From the data obtained by massive sequencing of the 16S rRNA gene on salivary samples and applying machine learning, the objective is the development of predictive models that will allow us to classify oral or systemic pathologies from a single sample per patient.</p> - PI24/00222 - María José Carreira Nouche, Inmaculada Tomás Carmona - Lara María Vázquez González, Berta Suárez Rodríguez
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