ArticleFrontiers in neuroscience2021
Identification of Diagnostic Markers for Major Depressive Disorder Using Machine Learning Methods.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Bioinformatics Analysis Reveals HIST1H2BH as a Novel Diagnostic Biomarker for Atrial Fibrillation-Related Cardiogenic Thromboembolic Stroke.Molecular biotechnology · 2025Pooled it
- Optimization of potential targets for antidepressant Chinese medicines: AI and multi-omics methods.Chinese medicine · 2026Review
- Identification of immune and major depressive disorder-related diagnostic markers for early nonalcoholic fatty liver disease by WGCNA and machine learning.Frontiers in bioinformatics · 2025Article
- Method Matters: Enhancing Voice-Based Depression Detection With a New Data Collection Framework.Depression and anxiety · 2025Article
- Advances and Challenges in Depression Marker Research.Neuropsychiatric disease and treatment · 2025Review
- Multiscale Contextual Mamba: Advancing Psychiatric Disorder Detection across Multisite Functional Magnetic Resonance Imaging Datasets via State Space Modeling.Health data science · 2025Article
- A deep learning model for prediction of autism status using whole-exome sequencing data.PLoS computational biology · 2024Article
- Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment.Diagnostics (Basel, Switzerland) · 2024Article
- WWC2 modulates GABACell reports · 2024Article
- Emerging Strategies in Drug Development and Clinical Care in the Era of Personalized and Precision Medicine.Pharmaceutics · 2024Article
- Identifying Depression Through Machine Learning Analysis of Omics Data: Scoping Review.JMIR nursing · 2024Article
- Article
- Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine.Scientific reports · 2024Article
- Identification and Analyses of Crucial Genes Associated with Pathogenesis of Major Depressive Disorder.Psychiatry and clinical psychopharmacology · 2023Article
- IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles.Bioinformatics (Oxford, England) · 2023Article
- EEG alpha band characteristics in patients with a depressive episode within recurrent and bipolar depression.Consortium psychiatricum · 2023Article
- Key Risk Genes Identified From the Postmortem Brain of Patients With Major Depressive Disorder and Their Potential Clinical Applications.The international journal of neuropsychopharmacology · 2023Article
- Ferroptosis-related genes as diagnostic markers for major depressive disorder and their correlations with immune infiltration.Frontiers in medicine · 2023Article
- Gene Signatures Associated with Temporal Rhythm as Diagnostic Markers of Major Depressive Disorder and Their Role in Immune Infiltration.International journal of molecular sciences · 2022Article
- Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.Briefings in bioinformatics · 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMajor depressive disorder (MDD) is a global health challenge that impacts the quality of patients' lives severely. The disorder can manifest in many forms with different combinations of symptoms, which makes its clinical diagnosis difficult. Robust biomarkers are greatly needed to improve diagnosis and to understand the etiology of the disease. The main purpose of this study was to create a predictive model for MDD diagnosis based on peripheral blood transcriptomes. MATERIALS AND
methodsWe collected nine RNA expression datasets for MDD patients and healthy samples from the Gene Expression Omnibus database. After a series of quality control and heterogeneity tests, 302 samples from six studies were deemed suitable for the study. R package "MetaOmics" was applied for systematic meta-analysis of genome-wide expression data. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic effectiveness of individual genes. To obtain a better diagnostic model, we also adopted the support vector machine (SVM), random forest (RF), k-nearest neighbors (kNN), and naive Bayesian (NB) tools for modeling, with the RF method being used for feature selection.
resultsOur analysis revealed six differentially expressed genes (
conclusionThis study provides new insights into potential biomarkers through meta-analysis of GEO data. Constructing different machine learning models based on these biomarkers could be a valuable approach for diagnosing MDD in clinical practice.
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.