ArticleFrontiers in neuroscience2022
A machine learning model for predicting patients with major depressive disorder: A study based on transcriptomic data.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Systematic review of unveiling the potential of AI using machine learning and deep learning methods in neurodegenerative diseases.PeerJ. Computer science · 2026Review
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9 authors.
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Abstract
Background: Identifying new biomarkers of major depressive disorder (MDD) would be of great significance for its early diagnosis and treatment. Herein, we constructed a diagnostic model of MDD using machine learning methods. Methods: The GSE98793 and GSE19738 datasets were obtained from the Gene Expression Omnibus database, and the limma R package was used to analyze differentially expressed genes (DEGs) in MDD patients. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to identify potential molecular functions and pathways. A protein-protein interaction network (PPI) was constructed, and hub genes were predicted. Random forest (RF) and artificial neural network (ANN) machine-learning algorithms were used to select variables and construct a robust diagnostic model. Results: A total of 721 DEGs were identified in peripheral blood samples of patients with MDD. GO and KEGG analyses revealed that the DEGs were mainly enriched in cytokines, defense responses to viruses, responses to biotic stimuli, immune effector processes, responses to external biotic stimuli, and immune systems. A PPI network was constructed, and CytoHubba plugins were used to screen hub genes. Furthermore, a robust diagnostic model was established using a RF and ANN algorithm with an area under the curve of 0.757 for the training model and 0.685 for the test cohort. Conclusion: We analyzed potential driver genes in patients with MDD and built a potential diagnostic model as an adjunct tool to assist psychiatrists in the clinical diagnosis and treatment of MDD.
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