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

Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysis

28 Nov 2024

Abstract excerpt

BACKGROUND: Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is very important. This study aims to analyze cases from multiple cohorts and get the signature that can distinguish LTBI from ATB. METHODS: Thirteen datasets were downloaded from the gene expression omnibus (GEO) database. Three datasets were selected as discovery datasets, and the hub genes were discovered through...

Read the complete abstract on PubMed

Topics

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

Share to Immune responses and vaccinations
Share to Tuberculosis Research and Epidemiology
Share to vaccines and immunoinformatics approaches

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 39609541
DOI: 10.1038/s41598-024-80072-3

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysisPMID 39609541Read ↗

Identification of hub genes and establishment of a diagnostic model in tuberculosis infectionSemantic proximity Read ↗

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

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

Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active TuberculosisSemantic proximity Read ↗

Diagnosis of pulmonary tuberculosis via identification of core genes and pathways utilizing blood transcriptional signatures: a multicohort analysisSemantic proximity Read ↗

AIM2 and FCGR1A, two potential diagnostic biomarkers for distinguishing infection statuses of Mycobacterium tuberculosisSemantic proximity Read ↗

Common gene signature model discovery and systematic validation for TB prognosis and response to treatmentSemantic proximity Read ↗

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

Gene signature discovery and systematic validation across diverse clinical cohorts for TB prognosis and response to treatmentSemantic proximity Read ↗

Host-response-based gene signatures for tuberculosis diagnosis: A systematic comparison of 16 signaturesSemantic proximity Read ↗

Development of a four autophagy-related gene signature for active tuberculosis diagnosisSemantic proximity Read ↗

Monocyte-related gene biomarkers for latent and active tuberculosisSemantic proximity Read ↗

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