背景痉挛型脑瘫,最常见的儿童发病致残疾病,估计儿童患病率为0.2%,是一种以僵硬运动为特征的复杂状况,肌肉挛缩,和异常步态会降低生活质量。痉挛型CP约占所有CP病例的83%,并且经常与其他复杂疾病同时发生。比如癫痫.估计42%的痉挛型CP病例同时发生癫痫。不幸的是,CP通常难以诊断。虽然大多数患有CP的孩子出生时就有它或在出生后立即获得它,许多患者在19个月后才被确诊,而CP诊断往往要到5岁才被确诊.需要新的生物信息学方法来早期识别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淆作用尚不清楚。我们评估了有或没有并发癫痫的CP患者的机器学习分类。结果从30名诊断为癫痫的研究参与者(n=4)中收集全血样本,痉挛CP(n=10),两者(n=8),或者都没有(n=8)。开发了一种新颖的支持向量机学习算法来识别甲基化基因座,该甲基化基因座能够在存在或不存在癫痫的情况下将CP与对照进行分类。该算法还用于测量鉴定的甲基化基因座的分类能力。数据预处理后,在CP和对照之间的二元比较中进行了重要的甲基化基因座的分离,以及在四向方案中,封装癫痫诊断。类似地评估分类能力。在有或没有将癫痫作为特征的情况下,对CP分类性能进行了评估。在4级比较中,F1得分中位数为0.67,和二元分类中的1.0,优于线性判别分析(分别为0.57和0.86)。结论这种新颖的算法能够将患有痉挛型CPA和/或癫痫的研究参与者与具有显著表现的对照进行分类。该算法有望在诊断甲基化基因座的甲基化数据中快速鉴定。在这个模型中,支持向量机在分类方面优于线性判别分析。在评估基于表观遗传学的CP诊断时,癫痫可能不是一个显著的混杂因素。
UNASSIGNED: Spastic cerebral palsy, the most common pediatric-onset disabling condition with an estimated prevalence of 0.2% in children, is a complex condition characterized by stiff movement, muscle contractures, and abnormal gait that can diminish quality of life. Spastic CP accounts for approximately 83% of all CP cases and frequently co-occurs with other complex conditions, like epilepsy. An estimated 42% of spastic CP cases have co-occurring epilepsy. Unfortunately, CP is often difficult to diagnose. Although most children with CP are born with it or acquire it immediately after birth, many are not identified until after 19 months of age with CP diagnosis often not confirmed until 5 years of age. New bioinformatic approaches to identify CP earlier are needed. Recent studies indicate that altered DNA methylation patterns associated with CP may have diagnostic value. The potential confounding effects of co-occurrent epilepsy on these patterns are not known. We evaluated machine learning classification of CP patients with or without co-occurring epilepsy.
UNASSIGNED: Whole blood samples were collected from 30 study participants diagnosed with epilepsy (n=4), spastic CP (n=10), both (n=8), or neither (n=8). A novel Support-Vector-Machine learning algorithm was developed to identify methylation loci that have ability to classify CP from controls in the presence or absence of epilepsy. This algorithm was also employed to measure classification ability of identified methylation loci. After preprocessing of data, isolation of important methylation loci was performed in a binary comparison between CP and controls, as well as in a 4-way scheme, encapsulating epilepsy diagnoses. The classification ability was similarly assessed. CP Classification performance was evaluated with and without inclusion of epilepsy as a feature. Median F1 scores were 0.67 in 4-class comparison, and 1.0 in the binary classification, outperforming Linear-Discriminant-Analysis (0.57 and 0.86, respectively).
UNASSIGNED: This novel algorithm was able to classify study participants with spastic CP and/or epilepsy from controls with significant performance. The algorithm shows promise for rapid identification in methylation data of diagnostic methylation loci. In this model, Support Vector Machines outperformed Linear Discriminant Analysis in classification. In the evaluation of epigenetics-based diagnostics for CP, epilepsy may not be a significant confounding factor.