Huntington’s disease (HD) is an inherited disorder caused by the polyglutamine (poly-Q) mutations of the HTT gene results in neurodegeneration characterized by chorea, loss of coordination, cognitive decline. However, HD pathogenesis is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of HD’s mechanism from machine learning is so far unrealized, majorly due to the lack of needed data density. To harness the knowledge of the HD pathogenesis from the expression profiles of postmortem prefrontal cortex samples of 157 HD and 157 controls, we used gene profiling ranking as the criteria to reduce the dimension to the order of magnitude of the sample size, followed by machine learning using the decision tree, rule induction, random forest, and generalized linear model. These four Machine learning models identified 66 potential HD-contributing genes, with the cross-validated accuracy of 90.79 ± 4.57%, 89.49 ± 5.20%, 90.45 ± 4.24%, and 97.46 ± 3.26%, respectively. The identified genes enriched the gene ontology of transcriptional regulation, inflammatory response, neuron projection, and the cytoskeleton. Moreover, three genes in the cognitive, sensory, and perceptual systems were also identified. The mutant HTT may interfere with both the expression and transport of these identified genes to promote the HD pathogenesis.

中文翻译：

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通过机器学习识别亨廷顿舞蹈病的贡献基因

亨廷顿舞蹈病（HD）是由HTT基因的聚谷氨酰胺（poly-Q）突变引起的遗传性疾病，导致神经退行性变，其特征是舞蹈病，失去协调能力，认知能力下降。但是，HD发病机制仍然难以捉摸。尽管可获得大量的生物学数据，但由于缺乏所需的数据密度，因此迄今为止尚未实现对机器学习对HD机制的全面了解。为了从157个HD和157个对照的死后前额叶皮层样本的表达谱中了解HD发病机理，我们使用基因谱分析排名作为将维度减少到样本大小的数量级的标准，然后进行机器学习使用决策树，规则归纳，随机森林和广义线性模型。这四个机器学习模型确定了66个潜在的HD贡献基因，交叉验证的准确度分别为90.79±4.57％，89.49±5.20％，90.45±4.24％和97.46±3.26％。鉴定出的基因丰富了转录调控，炎症反应，神经元投射和细胞骨架的基因本体。此外，还识别了认知，感觉和知觉系统中的三个基因。突变型HTT可能会干扰这些已鉴定基因的表达和运输，从而促进HD发病机制。和细胞骨架。此外，还识别了认知，感觉和知觉系统中的三个基因。突变型HTT可能会干扰这些已鉴定基因的表达和运输，从而促进HD发病机制。和细胞骨架。此外，还识别了认知，感觉和知觉系统中的三个基因。突变型HTT可能会干扰这些已鉴定基因的表达和运输，从而促进HD发病机制。