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

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genes

14 May 2024

Abstract excerpt

BACKGROUND: Major depressive disorder (MDD) and interstitial cystitis (IC) are two highly debilitating conditions that often coexist with reciprocal effect, significantly exacerbating patients' suffering. However, the molecular underpinnings linking these disorders remain poorly understood. METHODS: Transcriptomic data from GEO datasets including those of MDD and IC patients was systematically analyzed to develop...

Read the complete abstract on PubMed

Topics

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

Share to Dermatology and Skin Diseases
Share to Tryptophan and brain disorders
Share to Urinary Bladder and Prostate Research

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 38754597
DOI: 10.1016/j.jad.2024.05.061

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

An integrated machine learning framework for developing and validating a diagnostic model of major depressive disorder based on interstitial cystitis-related genesPMID 38754597Read ↗

Integrative bioinformatics and artificial intelligence analyses of transcriptomics data identified genes associated with major depressive disorders including NRG1Semantic proximity Read ↗

Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning MethodsSemantic proximity Read ↗

Machine Learning Reveals Common Regulatory Mechanisms Mediated by Autophagy-Related Genes in the Development of Inflammatory Bowel Disease and Major Depressive Disorder.Semantic proximity Read ↗

Identification of mitophagy-related biomarkers and immune infiltration in major depressive disorderSemantic proximity Read ↗

WITHDRAWN: Integrated bioinformatics and experimental validation identify KLHL6 as a potential shared immune-related regulator linking idiopathic pulmonary fibrosis and major depressive disorderSemantic proximity Read ↗

Identification of oxidative stress-related diagnostic marker genes and immune landscape in interstitial cystitis by bioinformatics and machine learningSemantic proximity Read ↗

Identification of Exercise Signature Genes Potentially Associated with Major Depressive Disorder by Combining Bioinformatics, Machine Learning, and Mendelian Randomization AnalysisSemantic proximity Read ↗

Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltrationSemantic proximity Read ↗

Screening and identification of key biomarkers of depression using bioinformaticsSemantic proximity Read ↗

Identification and Experimental Validation of Mitochondria-Related Biomarkers for Depression and Neuropathic PainSemantic proximity Read ↗

Genetic association and drug target exploration of inflammation-related proteins with risk of major depressive disorder.Semantic proximity Read ↗

Peripheral Blood Mononuclear Cell Biomarkers for Major Depressive Disorder: A Transcriptomic ApproachSemantic proximity Read ↗

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