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Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm

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Abstract

Schizophrenia is a debilitating psychiatric disorder that can significantly affect a patient’s quality of life and lead to permanent brain damage. Although medical research has identified certain genetic risk factors, the specific pathogenesis of the disorder remains unclear. Despite the prevalence of research employing magnetic resonance imaging, few studies have focused on the gene level and gene expression profile involving a large number of screened genes. However, the high dimensionality of genetic data presents a great challenge to accurately modeling the data. To tackle the current challenges, this study presents a novel feature selection strategy that utilizes heuristic feature fusion and a multi-objective optimization genetic algorithm. The goal is to improve classification performance and identify the key gene subset for schizophrenia diagnostics. Traditional gene screening techniques are inadequate for accurately determining the precise number of key genes associated with schizophrenia. Our innovative approach integrates a filter-based feature selection method to reduce data dimensionality and a multi-objective optimization genetic algorithm for improved classification tasks. By combining the filtering and wrapper methods, our strategy leverages their respective strengths in a deliberate manner, leading to superior classification accuracy and a more efficient selection of relevant genes. This approach has demonstrated significant improvements in classification results across 11 out of 14 relevant datasets. The performance on the remaining three datasets is comparable to the existing methods. Furthermore, visual and enrichment analyses have confirmed the practicality of our proposed method as a promising tool for the early detection of schizophrenia.

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Data availability

The data underlying this article are available in Gene Expression Omnibus (GEO) publicly accessible database. All the datasets were derived from sources in the public domain:

GSE12649: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12649.

GSE12654: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12654.

GSE12679: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12679.

GSE17612: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17612.

GSE21138: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21138.

GSE21935: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21935.

GSE26927: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26927.

GSE35974: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35974.

GSE35977: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35977.

GSE35978: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35978.

GSE53987: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53987.

GSE62191: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62191.

GSE87610: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE87610.

GSE93987: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93987.

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Funding

The work was jointly supported by the Zhejiang Provincial Natural Science Foundation of China (No.LY21F020017), National Natural Science Foundations of China (No. U20A20386), Science and Technology Program of Zhejiang Province (No. 2022C03043, 2022C01016), GuangDong Basic and Applied Basic Research Foundation (No.2022A1515110570).

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Authors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Zhixiong Chen, Ruiquan Ge, Feiwei Qin & Gangyong Jia

  2. Hangzhou Institute of Advanced Technology, Hangzhou, China

    Ruiquan Ge & Xiaopeng Fan

  3. Shenzhen Research Institute of Big Data, Shenzhen, China

    Changmiao Wang

  4. Computer Science Department, Misr Higher Institute for Commerce and Computers, Mansoura, Egypt

    Ahmed Elazab

  5. School of Artificial Intelligence, Zhejiang College of Security Technology, Wenzhou, China

    Xianjun Fu

  6. School of Information Science and Engineering, Yunnan University, Kunming, China

    Wenwen Min

  7. Key Laboratory of Discrete Industrial Internet of Things of Zhejiang Province, Hangzhou, China

    Ruiquan Ge

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  1. Zhixiong Chen
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Contributions

ZC: Methodology, software, visualization, Writing—original draft. RG: Investigation, methodology, Writing—review & editing, funding acquisition, project administration. CW: Methodology, formal analysis, supervision, funding acquisition. AE: Formal analysis, investigation, data curation, Writing—review & editing. XF: Formal analysis, review & editing, visualization. WM: Conceptualization, Writing—review & editing. FQ: Conceptualization, formal analysis, Writing—review & editing, funding acquisition. GJ：Data curation, review & editing. XF: Investigation, data curation, Writing—review & editing.

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Correspondence to Ruiquan Ge.

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Chen, Z., Ge, R., Wang, C. et al. Identification of important gene signatures in schizophrenia through feature fusion and genetic algorithm. Mamm Genome (2024). https://doi.org/10.1007/s00335-024-10034-7

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