新颖的DNA甲基化模式机器学习分析算法可识别并发癫痫的脑瘫患者

Jonathan Hicks, Karyn Robinson, Stephanie Lee, Adam Marsh, Robert Akins
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摘要

背景 痉挛性脑瘫是儿科最常见的致残性疾病,在儿童中的发病率约为 0.2%,是一种以运动僵硬、肌肉挛缩和步态异常为特征的复杂疾病,会降低生活质量。痉挛型脊髓灰质炎约占所有脊髓灰质炎病例的 83%,并经常与癫痫等其他复杂疾病并发。据估计,42% 的痉挛型 CP 病例同时伴有癫痫。不幸的是,CP 通常很难诊断。虽然大多数 CP 患儿是先天性或出生后即患此病,但许多患儿直到 19 个月大后才被发现,CP 诊断通常要到 5 岁时才能确诊。我们需要新的生物信息学方法来更早地识别 CP。最近的研究表明,与 CP 相关的 DNA 甲基化模式改变可能具有诊断价值。并发癫痫对这些模式的潜在混杂效应尚不清楚。我们对有无并发癫痫的 CP 患者进行了机器学习分类评估。结果 收集了 30 名被诊断为癫痫(4 人)、痉挛性 CP(10 人)、两者均有(8 人)或两者均无(8 人)的研究参与者的全血样本。研究人员开发了一种新颖的支持向量机器学习算法,以确定哪些甲基化位点有能力在存在或不存在癫痫的情况下将痉挛性脊柱炎与对照组进行分类。该算法还用于测量已识别甲基化位点的分类能力。在对数据进行预处理后,分离出重要的甲基化位点,在 CP 和对照组之间进行二元比较,以及在包含癫痫诊断的四元方案中进行比较。对分类能力也进行了类似的评估。在将癫痫作为一个特征纳入和不纳入的情况下,对 CP 分类性能进行了评估。4 类比较的中位 F1 分数为 0.67,二元分类的中位 F1 分数为 1.0,分别优于线性判别分析(0.57 和 0.86)。结论 这种新型算法能够将患有痉挛性脊柱炎和/或癫痫的研究对象从对照组中分类出来,而且效果显著。该算法有望在甲基化数据中快速识别诊断甲基化位点。在该模型中,支持向量机的分类效果优于线性判别分析。在对基于表观遗传学的 CP 诊断进行评估时,癫痫可能不是一个重要的混杂因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel Machine Learning of DNA Methylation Patterns to Diagnose Complex Disease: Identification of Cerebral Palsy with Concurrent Epilepsy.

Spastic cerebral palsy (CP) is a common pediatric-onset disability with an estimated prevalence of 0.2%. It is a complex condition characterized by muscle stiffness, contractures, and abnormal movement. Spastic CP is difficult to diagnose. Although nearly all affected children are born with it or acquire it immediately after birth, many are not identified until after 19 months of age with the diagnosis often not confirmed until 5 years of age. In addition, CP frequently co-occurs with other complex conditions that can complicate diagnosis and treatment. For example, an estimated 42% of spastic CP cases have co-occurring epilepsy. Recent studies indicate that altered DNA methylation patterns in peripheral blood cells are associated with CP and may have diagnostic value.Accordingly, the purpose of this study is to assess the diagnostic value of methylation in CP with more complex disease states. We evaluated machine learning classification for detecting CP based on DNA methylation pattern analysis in the context of co-occurrent epilepsy. Blood samples from 30 study participants diagnosed with epilepsy (n=4), spastic CP (n=10), both (n=8), or neither (n=8) were analyzed by Illumina MethylationEpic arrays. A novel machine learning algorithm using a Support Vector Machine (SVM) or Linear Discriminant Analysis (LDA) was developed to identify methylation loci that classified CP from controls and to measure the classification ability of identified methylation loci. The isolation of informative methylation loci was performed in a binary comparison between CP and controls, as well as in a 4-way comparison that included epilepsy. Median F1 scores for SVM-based analysis were 0.67 in 4-class comparison, and 1.0 in the binary classification. SVM outperformed LDA (median F1 0.57 and 0.86, respectively). Overall, the novel machine learning based algorithm was able to classify study participants with spastic CP and/or epilepsy from controls with significant performance.

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