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鉴定基因水平甲基化用于疾病预测

Jisha Augustine¹, A S Jereesh²

¹Bioinformatics Lab, Department of Computer Science, Cochin University of Science and Technology, Cochin, Kerala, 682022, India. jishaaugustine@cusat.ac.in.

Interdisciplinary sciences, computational life sciences

|August 21, 2023

概括

我们开发了一种新的方法,监督UMAP辅助基因水平甲基化 (sUAGM),用于分析基因水平DNA甲基化以预测疾病. 这种方法准确地识别了与疾病相关的基因,优于现有的方法.

科学领域:

表观遗传学在表观遗传学中,表观遗传学是指表观遗传学.
基因组学就是基因组学.
计算生物学计算生物学

背景情况:

基因甲基化是基因表达的关键表观遗传调节剂,影响染色质结构,并与各种疾病有关.
全表观基因组关联研究 (EWAS) 确定与表型相关的甲基化位点,但基因水平甲基化分析仍然具有挑战性.
精确的基因水平DNA甲基化量化对于理解疾病机制和开发预测生物标志物至关重要.

研究的目的:

提出一种新的方法,监督UMAP辅助基因水平甲基化 (sUAGM),用于计算基因水平DNA甲基化.
用机器学习算法评估sUAGM在疾病预测中的有效性.
为了比较sUAGM的性能与现有的基因水平和现场水平的DNA甲基化分析方法.

主要方法:

开发了监督的UMAP辅助基因水平甲基化 (sUAGM) 方法,利用监督的统一多重近似和投影 (UMAP) 来减少维度.
应用sUAGM从三个不同的血液DNA甲基化数据集生成基因水平甲基化值.
使用分类准确度,F-1分数,MCC,Kappa,CSI和Jaccard指数以及各种特征选择和分类算法评估性能,包括带有线性内核 (SVML) 和递归特征消除 (RFE) 的支持向量机.

主要成果:

通过sUAGM方法,特别是使用SVML和RFE,在所有三组数据中实现了100%的准确性和F1得分.
sUAGM在疾病预测准确性和效率 (更少的基因) 中超过了现有的基因层和地点层方法.
从帕金森病数据中对顶级基因的功能分析揭示了重要的疾病关联.

结论:

sUAGM方法为基因水平的DNA甲基化分析和疾病预测提供了强大而准确的方法.
与目前的方法相比,sUAGM表现出更高的性能,提供了一种更有效的方法来识别与疾病相关的基因.
这种方法有望促进表观遗传生物标记物的发现,并了解DNA甲基化在复杂疾病中的作用.

关键词:

基因甲基化 DNA 甲基化疾病预测疾病预测基因水平的甲基化基因水平的甲基化机器学习机器学习这就是UMAP UMAP.

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