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- W2970322015 abstract "Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes." @default.
- W2970322015 created "2019-09-05" @default.
- W2970322015 creator A5002688054 @default.
- W2970322015 creator A5025077602 @default.
- W2970322015 creator A5026321844 @default.
- W2970322015 creator A5046736908 @default.
- W2970322015 creator A5064600744 @default.
- W2970322015 creator A5076748700 @default.
- W2970322015 date "2019-08-31" @default.
- W2970322015 modified "2023-10-12" @default.
- W2970322015 title "Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes" @default.
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