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BioMed Research International
Volume 2017, Article ID 7860506, 7 pages
https://doi.org/10.1155/2017/7860506
Research Article

The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures

1School of Mechanical Engineering, Xi’an Jiao Tong University, State Key Laboratory of Manufacturing System Engineering, Xi’an 710049, China
2College of Medicine & Forensic, Health Science Center, Xi’an Jiaotong University, Xi’an 710061, China
3Department of Obstetrics and Gynecology, The First Affiliated Hospital, Xi’an Jiaotong University, Xi’an 710061, China

Correspondence should be addressed to Bao Zhang; nc.ude.utjx.liam@418_oabgnahz and Jing Fang; moc.621@39988gnijgnaf

Received 27 September 2016; Accepted 30 November 2016; Published 9 February 2017

Academic Editor: Marco Fichera

Copyright © 2017 Hang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.