A Novel Machine-learning Model to Classify Schizophrenia Using Methylation Data Based on Gene Expression

被引:0
作者
Vijayakumar, Karthikeyan A. [1 ,2 ]
Cho, Gwang-Won [1 ,2 ,3 ]
机构
[1] Chosun Univ, Coll Nat Sci, Dept Biol Sci, 309 Pilmun Daero, Gwangju 501759, South Korea
[2] Chosun Univ, Dept Integrat Biol Sci, BK21 FOUR Educ Res Grp Age Associated Disorder Con, Gwangju 501759, South Korea
[3] Chosun Univ, Basic Sci Inst, Gwangju 61452, South Korea
基金
新加坡国家研究基金会;
关键词
Schizophrenia; gene expression; DNA methylation; multi omics; machine learning; DNA METHYLATION; HYPOTHESIS; NORMALIZATION;
D O I
10.2174/0115748936293407240222113019
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Introduction The recent advancement in artificial intelligence has compelled medical research to adapt the technologies. The abundance of molecular data and AI technology has helped in explaining various diseases, even cancers. Schizophrenia is a complex neuropsychological disease whose etiology is unknown. Several gene-wide association studies attempted to narrow down the cause of the disease but did not successfully point out the mechanism behind the disease. There are studies regarding the epigenetic changes in the schizophrenia disease condition, and a classification machine-learning model has been trained using the blood methylation data.Methods In this study, we have demonstrated a novel approach to elucidating the molecular cause of the disease. We used a two-step machine-learning approach to determine the causal molecular markers. By doing so, we developed classification models using both gene expression microarray and methylation microarray data.Results Our models, because of our novel approach, achieved good classification accuracy with the available data size. We analyzed the important features, and they add up as evidence for the glutamate hypothesis of schizophrenia.Conclusion In this way, we have demonstrated explaining a disease through machine learning models.
引用
收藏
页码:31 / 45
页数:15
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