An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile

An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile

A better understanding of the response against Tuberculosis (TB) infection is required to accurately identify the individuals with an active or a latent TB infection (LTBI) and also those LTBI patients at higher risk of developing active TB. In this work, we have used the information obtained from s...

Full description

Bibliographic Details
Main Authors: Olivia Estévez, Luis Anibarro, Elina Garet, Ángeles Pallares, Laura Barcia, Laura Calviño, Cremildo Maueia, Tufária Mussá, Florentino Fdez-Riverola, Daniel Glez-Peña, Miguel Reboiro-Jato, Hugo López-Fernández, Nuno A. Fonseca, Rajko Reljic, África González-Fernández
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-07-01
Series:Frontiers in Immunology
Subjects:
tuberculosis
latent tuberculosis
RNA-seq
machine-learning
TB progression
Online Access:https://www.frontiersin.org/article/10.3389/fimmu.2020.01470/full

Internet

https://www.frontiersin.org/article/10.3389/fimmu.2020.01470/full

Similar Items