ArticleFrontiers in neuroscience2023
Machine learning algorithms assisted identification of post-stroke depression associated biological features.
Article in Frontiers in neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Machine Learning in Predicting Post-Stroke Depression: A Systematic Review and Meta-Analysis.Brain and behavior · 2025Pooled it
- Clinical prediction models for post-stroke depression: a systematic review and meta-analysis.Frontiers in psychiatry · 2025Pooled it
- Mechanisms and integrative machine learning approaches to blood-brain barrier biomarker profiling for personalized ischemic stroke management.Physiological reports · 2026Review
- Progress and challenges in exploring the pathogenesis of depressive disorders via RNA sequencing.Frontiers in genetics · 2026Review
- High-Resolution Spatiotemporal Mapping of Cerebral Metabolism During Middle-Cerebral-Artery Occlusion/Reperfusion Progression: Preliminary Insights.Biomolecules · 2025Article
- Development and interpretation of a machine learning risk prediction model for post-stroke depression in a Chinese population.Scientific reports · 2025Article
- Establishment and validation of a model for predicting depression risk in stroke patients.BMC psychiatry · 2025Article
- Gene expression profiles of endothelium, microglia and oligodendrocytes in hippocampus of post-stroke depression rat at single cell resolution.Molecular psychiatry · 2025Article
- Identification of novel inflammatory response-related biomarkers in patients with ischemic stroke based on WGCNA and machine learning.European journal of medical research · 2025Article
- The Impact of Post-stroke Depression and Physical Fatigue on Functional Status.Actas espanolas de psiquiatria · 2025Article
- A network-based approach to discover diagnostic metabolite markers associated with depressive features for major depressive disorder.Frontiers in psychiatry · 2025Article
- Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression.Frontiers in psychiatry · 2025Article
- Toward precision psychological rehabilitation: predicting CBT efficacy in post-stroke depression using machine learning.Frontiers in psychiatry · 2025Article
- Exploring a new signature for lung adenocarcinoma: analyzing cuproptosis-related genes through Integrated single-cell and bulk RNA sequencing.Discover oncology · 2024Article
- Identification and validation of platelet-related diagnostic markers and potential drug screening in ischemic stroke by integrating comprehensive bioinformatics analysis and machine learning.Frontiers in immunology · 2023Article
- An interpretable analysis of the depressive status and its influencing factors in elderly patients with stroke.Frontiers in neurologyArticle
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11 authors.
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Abstract
Objectives: Post-stroke depression (PSD) is a common and serious psychiatric complication which hinders functional recovery and social participation of stroke patients. Stroke is characterized by dynamic changes in metabolism and hemodynamics, however, there is still a lack of metabolism-associated effective and reliable diagnostic markers and therapeutic targets for PSD. Our study was dedicated to the discovery of metabolism related diagnostic and therapeutic biomarkers for PSD. Methods: Expression profiles of GSE140275, GSE122709, and GSE180470 were obtained from GEO database. Differentially expressed genes (DEGs) were detected in GSE140275 and GSE122709. Functional enrichment analysis was performed for DEGs in GSE140275. Weighted gene co-expression network analysis (WGCNA) was constructed in GSE122709 to identify key module genes. Moreover, correlation analysis was performed to obtain metabolism related genes. Interaction analysis of key module genes, metabolism related genes, and DEGs in GSE122709 was performed to obtain candidate hub genes. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and random forest, were used to identify signature genes. Expression of signature genes was validated in GSE140275, GSE122709, and GSE180470. Gene set enrichment analysis (GSEA) was applied on signature genes. Based on signature genes, a nomogram model was constructed in our PSD cohort (27 PSD patients vs. 54 controls). ROC curves were performed for the estimation of its diagnostic value. Finally, correlation analysis between expression of signature genes and several clinical traits was performed. Results: Functional enrichment analysis indicated that DEGs in GSE140275 enriched in metabolism pathway. A total of 8,188 metabolism associated genes were identified by correlation analysis. WGCNA analysis was constructed to obtain 3,471 key module genes. A total of 557 candidate hub genes were identified by interaction analysis. Furthermore, two signature genes (SDHD and FERMT3) were selected using LASSO and random forest analysis. GSEA analysis found that two signature genes had major roles in depression. Subsequently, PSD cohort was collected for constructing a PSD diagnosis. Nomogram model showed good reliability and validity. AUC values of receiver operating characteristic (ROC) curve of SDHD and FERMT3 were 0.896 and 0.964. ROC curves showed that two signature genes played a significant role in diagnosis of PSD. Correlation analysis found that SDHD ( Conclusion: A total of 557 metabolism associated candidate hub genes were obtained by interaction with DEGs in GSE122709, key modules genes, and metabolism related genes. Based on machine learning algorithms, two signature genes (SDHD and FERMT3) were identified, they were proved to be valuable therapeutic and diagnostic biomarkers for PSD. Early diagnosis and prevention of PSD were made possible by our findings.
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