Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysis

18 Jan 2024

Abstract excerpt

Background Apoptosis is associated with the pathogenesis of Mycobacterium tuberculosis infection. This study aims to identify apoptosis-related genes as biomarkers for differentiating active tuberculosis (ATB) from latent tuberculosis infection (LTBI). Methods The tuberculosis (TB) datasets (GSE19491, GSE62525, and GSE28623) were downloaded from the Gene Expression Omnibus (GEO) database. The diagnostic...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biomarkers in Disease Mechanisms
Share to RNA modifications and cancer
Share to Tuberculosis Research and Epidemiology

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 38312744
DOI: 10.3389/fcimb.2024.1285493

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Identification of apoptosis-related gene signatures as potential biomarkers for differentiating active from latent tuberculosis via bioinformatics analysisPMID 38312744Read ↗

Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics ApproachSemantic proximity Read ↗

Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysisSemantic proximity Read ↗

Human gene expression profiling identifies key therapeutic targets in tuberculosis infection: A systematic network meta-analysis.Semantic proximity Read ↗

DHX36, BAX, and ARPC1B May Be Critical for the Diagnosis and Treatment of TuberculosisSemantic proximity Read ↗

Candidate serum protein biomarkers for active pulmonary tuberculosis diagnosis in tuberculosis endemic settingsSemantic proximity Read ↗

Identification of Gene Expression Biomarkers Predictive of Latent Tuberculosis Infection Using Machine Learning ApproachesSemantic proximity Read ↗

Transcriptional Profiling of Human Peripheral Blood Mononuclear Cells Identifies Diagnostic Biomarkers That Distinguish Active and Latent TuberculosisSemantic proximity Read ↗

Gene expression profiling identifies candidate biomarkers for active and latent tuberculosisSemantic proximity Read ↗

Identification potential biomarkers in pulmonary tuberculosis and latent infection based on bioinformatics analysisSemantic proximity Read ↗

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learningSemantic proximity Read ↗

Bioinformatics-led discovery of ferroptosis-associated diagnostic biomarkers and molecule subtypes for tuberculosis patientsSemantic proximity Read ↗

Screening of extra-pulmonary tuberculosis signature genes and analysis of correlation with immune cellsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.