ArticleFrontiers in immunology2020
An RNA-seq Based Machine Learning Approach Identifies Latent Tuberculosis Patients With an Active Tuberculosis Profile.
Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- RNA-seq research landscape in Africa: systematic review reveals disparities and opportunities.European journal of medical research · 2023Pooled it
- Reactivation and Management of Endogenous Latent Herpesviruses in the Spaceflight Environment.Current microbiology · 2026Review
- Detection for New Biomarkers of Tuberculosis Infection Activity Using Machine Learning Methods.Diseases (Basel, Switzerland) · 2026Review
- Functional analysis of distinct factors linked to the development of latent to active tuberculosis.Frontiers in cellular and infection microbiology · 2026Review
- A predictive model for evaluating the risk of latent tuberculosis relapse via machine learning.BMC infectious diseases · 2025Article
- Screening for latent tuberculosis infection in patients with chronic kidney disease: a review of evidence and current practice in the UK.Clinical kidney journal · 2025Review
- Identification and assessment of hub genes and miRNAs coregulatory associated with immune infiltrations and drug interactions in latent tuberculosis based on MicroarrayData analysis, molecular docking, and dynamic simulation.Biochemistry and biophysics reports · 2025Article
- An integrative and comprehensive analysis of blood transcriptomes combined with machine learning models reveals key signatures for tuberculosis diagnosis and risk stratification.Frontiers in microbiology · 2025Article
- From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning.Military Medical Research · 2023Review
- Single-Cell Transcriptomics ofCells · 2023Review
- Challenges and the Way forward in Diagnosis and Treatment of Tuberculosis Infection.Tropical medicine and infectious disease · 2023Review
- Serological analysis reveals differential antibody responses between TB patients and latently infected individuals from the TB endemic country of Mozambique.Frontiers in medicine · 2023Article
- A protein signature associated with active tuberculosis identified by plasma profiling and network-based analysis.iScience · 2022Article
- Review
- The spectrum of tuberculosis described as differential DNA methylation patterns in alveolar macrophages and alveolar T cells.Clinical epigenetics · 2022Article
- Article
- Article
- Contribution and Future of High-Throughput Transcriptomics in Battling Tuberculosis.Frontiers in microbiology · 2022Review
- Whole transcriptome sequencing reveals neutrophils' transcriptional landscape associated with active tuberculosis.Frontiers in immunology · 2022Article
- Implications of Using Host Response-Based Molecular Diagnostics on the Management of Bacterial and Viral Infections: A Review.Frontiers in medicine · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 studying the gene expression profile of active TB patients and their infected -LTBI- or uninfected -NoTBI- contacts, recruited in Spain and Mozambique, to build a class-prediction model that identifies individuals with a TB infection profile. Following this approach, we have identified several genes and metabolic pathways that provide important information of the immune mechanisms triggered against TB infection. As a novelty of our work, a combination of this class-prediction model and the direct measurement of different immunological parameters, was used to identify a subset of LTBI contacts (called
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.