ArticleBioMed research international2017
The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures.
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 11 citations in OpenAlex.
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- Mitochondrial Oxidative Phosphorylation System Dysfunction in Schizophrenia.International journal of molecular sciences · 2025Review
- Integrated Transcriptome Analysis Reveals Novel Molecular Signatures for Schizophrenia Characterization.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A primer on the use of machine learning to distil knowledge from data in biological psychiatry.Molecular psychiatry · 2024Review
- Experimental exposure to gasohol impairs sperm quality with recognition of the classification pattern of exposure groups by machine learning algorithms.Environmental science and pollution research international · 2019Article
- Evaluation of genotoxic effects in Brazilian agricultural workers exposed to pesticides and cigarette smoke using machine-learning algorithms.Environmental science and pollution research international · 2018Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microarray analysis of gene expression is often used to diagnose different types of disease. Many studies report remarkable achievements in nervous system disease. Clinical diagnosis of schizophrenia (SCZ) still depends on doctors' experience, which is unreliable and needs to be more objective and quantified. To solve this problem, we collected whole blood gene expression data from four studies, including 152 individuals with schizophrenia (SCZ) and 138 normal controls in different regions. The correlation-based feature selection (CFS, one of the machine learning methods) algorithm was applied in this study, and 103 significantly differentially expressed genes between patients and controls, called "feature genes," were selected; then, a model for SCZ diagnosis was built. The samples were subdivided into 10 groups, and cross-validation showed that the model we constructed achieved nearly 100% classification accuracy. Mathematical evaluation of the datasets before and after data processing proved the effectiveness of our algorithm. Feature genes were enriched in Parkinson's disease, oxidative phosphorylation, and TGF-beta signaling pathways, which were previously reported to be associated with SCZ. These results suggest that the analysis of gene expression in whole blood by our model could be a useful tool for diagnosing SCZ.
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