Identification of gene-level methylation for disease prediction.

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jisha Augustine, A S Jereesh
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引用次数: 0

Abstract

DNA methylation is an epigenetic alteration that plays a fundamental part in governing gene regulatory processes. The DNA methylation mechanism affixes methyl groups to distinct cytosine residues, influencing chromatin architectures. Multiple studies have demonstrated that DNA methylation's regulatory effect on genes is linked to the beginning and progression of several disorders. Researchers have recently uncovered thousands of phenotype-related methylation sites through the epigenome-wide association study (EWAS). However, combining the methylation levels of several sites within a gene and determining the gene-level DNA methylation remains challenging. In this study, we proposed the supervised UMAP Assisted Gene-level Methylation method (sUAGM) for disease prediction based on supervised UMAP (Uniform Manifold Approximation and Projection), a manifold learning-based method for reducing dimensionality. The methylation values at the gene level generated using the proposed method are evaluated by employing various feature selection and classification algorithms on three distinct DNA methylation datasets derived from blood samples. The performance has been assessed employing classification accuracy, F-1 score, Mathews Correlation Coefficient (MCC), Kappa, Classification Success Index (CSI) and Jaccard Index. The Support Vector Machine with the linear kernel (SVML) classifier with Recursive Feature Elimination (RFE) performs best across all three datasets. From comparative analysis, our method outperformed existing gene-level and site-level approaches by achieving 100% accuracy and F1-score with fewer genes. The functional analysis of the top 28 genes selected from the Parkinson's disease dataset revealed a significant association with the disease.

Abstract Image

用于疾病预测的基因水平甲基化鉴定。
DNA甲基化是一种表观遗传学改变,在控制基因调控过程中起着重要作用。DNA甲基化机制将甲基固定在不同的胞嘧啶残基上,影响染色质结构。多项研究表明,DNA甲基化对基因的调节作用与几种疾病的开始和进展有关。研究人员最近通过表观基因组广泛关联研究(EWAS)发现了数千个表型相关的甲基化位点。然而,结合基因内几个位点的甲基化水平并确定基因水平的DNA甲基化仍然具有挑战性。在本研究中,我们提出了基于监督UMAP(统一流形近似和投影)的监督UMAP辅助基因水平甲基化方法(sUAGM),这是一种基于流形学习的降维方法。通过在源自血液样本的三个不同的DNA甲基化数据集上使用各种特征选择和分类算法来评估使用所提出的方法生成的基因水平的甲基化值。使用分类准确度、F-1评分、马修斯相关系数(MCC)、Kappa、分类成功指数(CSI)和Jaccard指数对性能进行了评估。具有线性核(SVML)分类器和递归特征消除(RFE)的支持向量机在所有三个数据集中表现最好。从比较分析来看,我们的方法优于现有的基因水平和位点水平方法,用更少的基因实现了100%的准确率和F1分数。从帕金森病数据集中选出的前28个基因的功能分析揭示了与该疾病的显著关联。
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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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