基于肿瘤组织反卷积和SVM算法的疾病预测分析
Disease Prediction Analysis Based on Tumor Tissue Deconvolution and SVM Algorithm
DOI: 10.12677/AAM.2022.117468, PDF,    国家自然科学基金支持
作者: 周素素, 张伟伟*：东华理工大学理学院，江西 南昌
关键词: 疾病预测；肿瘤组织异质性；反卷积；SVM；Disease Prediction； Tumor Tissue Heterogeneity； Deconvolution； SVM
摘要: 预后和诊断在疾病的预防和治疗中起着至关重要的作用。本文基于临床肿瘤组织的DNA甲基化芯片数据，利用反卷积算法对肿瘤组织进行分解，将估计得到的肿瘤组织中各细胞类型所占比例和细胞类型特异性的甲基化位点作为生物标志物，利用SVM算法构建该疾病的预测模型。TCGA肺腺癌、肾透明细胞癌的数据分析表明，所提方法在预测精确度和鲁棒性上都优于常用算法。
Abstract: Prognosis and diagnosis play a crucial role in the prevention and treatment of diseases. Based on the DNA methylation microarray data of clinical tumor tissue, this paper decomposes the tumor tissue by using the deconvolution algorithm, takes the estimated cell type proportions and cell type-specific methylation sites as biomarkers, and constructs the disease prediction model by using SVM algorithm. The data analysis of TCGA lung adenocarcinoma and renal clear cell carcinoma shows that the proposed method is superior to the common algorithms in accuracy and robustness.
文章引用：周素素, 张伟伟. 基于肿瘤组织反卷积和SVM算法的疾病预测分析[J]. 应用数学进展, 2022, 11(7): 4419-4426. https://doi.org/10.12677/AAM.2022.117468

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