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Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning
المؤلفون المشاركون
Ren, Jianting
Zhang, Bo
Wei, Dongfeng
Zhang, Zhanjun
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-11، 11ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-03-27
دولة النشر
مصر
عدد الصفحات
11
التخصصات الرئيسية
الملخص EN
Background.
Alzheimer’s disease (AD) is a neurodegenerative disorder and characterized by the cognitive impairments.
It is essential to identify potential gene biomarkers for AD pathology.
Methods.
DNA methylation expression data of patients with AD were downloaded from the Gene Expression Omnibus (GEO) database.
Differentially methylated sites were identified.
The functional annotation analysis of corresponding genes in the differentially methylated sites was performed.
The optimal diagnostic gene biomarkers for AD were identified by using random forest feature selection procedure.
In addition, receiver operating characteristic (ROC) diagnostic analysis of differentially methylated genes was performed.
Results.
A total of 10 differentially methylated sites including 5 hypermethylated sites and 5 hypomethylated sites were identified in AD.
There were a total of 8 genes including thioredoxin interacting protein (TXNIP), noggin (NOG), regulator of microtubule dynamics 2 (FAM82A1), myoneurin (MYNN), ankyrin repeat domain 34B (ANKRD34B), STAM-binding protein like 1, ALMalpha (STAMBPL1), cyclin-dependent kinase inhibitor 1C (CDKN1C), and coronin 2B (CORO2B) that correspond to 10 differentially methylated sites.
The cell cycle (FDR=0.0284087) and TGF-beta signaling pathway (FDR=0.0380372) were the only two significantly enriched pathways of these genes.
MYNN was selected as optimal diagnostic biomarker with great diagnostic value.
The random forests model could effectively predict AD.
Conclusion.
Our study suggested that MYNN could be served as optimal diagnostic biomarker of AD.
Cell cycle and TGF-beta signaling pathway may be associated with AD.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Ren, Jianting& Zhang, Bo& Wei, Dongfeng& Zhang, Zhanjun. 2020. Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning. BioMed Research International،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1137409
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Ren, Jianting…[et al.]. Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning. BioMed Research International No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1137409
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Ren, Jianting& Zhang, Bo& Wei, Dongfeng& Zhang, Zhanjun. Identification of Methylated Gene Biomarkers in Patients with Alzheimer’s Disease Based on Machine Learning. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1137409
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1137409
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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