Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network
المؤلفون المشاركون
Xie, Ning-Ning
Wang, Fang-Fang
Zhou, Jue
Liu, Chang
Qu, Fan
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-08-20
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies.
However, few studies have tried to develop a diagnostic model based on gene biomarkers.
In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model.
We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets.
Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples.
Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively.
Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS.
Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC).
Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset.
To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Xie, Ning-Ning& Wang, Fang-Fang& Zhou, Jue& Liu, Chang& Qu, Fan. 2020. Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network. BioMed Research International،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1132627
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Xie, Ning-Ning…[et al.]. Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network. BioMed Research International No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1132627
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Xie, Ning-Ning& Wang, Fang-Fang& Zhou, Jue& Liu, Chang& Qu, Fan. Establishment and Analysis of a Combined Diagnostic Model of Polycystic Ovary Syndrome with Random Forest and Artificial Neural Network. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1132627
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1132627
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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