Table2_Identifying Key MicroRNA Signatures for Neurodegenerative Diseases With Machine Learning Methods.XLSX

Neurodegenerative diseases, including Alzheimer’s disease (AD), Parkinson’s disease, and many other disease types, cause cognitive dysfunctions such as dementia via the progressive loss of structure or function of the body’s neurons. However, the etiology of these diseases remains unknown, and diagnosing less common cognitive disorders such as vascular dementia (VaD) remains a challenge. In this work, we developed a machine-leaning-based technique to distinguish between normal control (NC), AD, VaD, dementia with Lewy bodies, and mild cognitive impairment at the microRNA (miRNA) expression level. First, unnecessary miRNA features in the miRNA expression profiles were removed using the Boruta feature selection method, and the retained feature sets were sorted using minimum redundancy maximum relevance and Monte Carlo feature selection to provide two ranking feature lists. The incremental feature selection method was used to construct a series of feature subsets from these feature lists, and the random forest and PART classifiers were trained on the sample data consisting of these feature subsets. On the basis of the model performance of these classifiers with different number of features, the best feature subsets and classifiers were identified, and the classification rules were retrieved from the optimal PART classifiers. Finally, the link between candidate miRNA features, including hsa-miR-3184-5p, has-miR-6088, and has-miR-4649, and neurodegenerative diseases was confirmed using recently published research, laying the groundwork for more research on miRNAs in neurodegenerative diseases for the diagnosis of cognitive impairment and the understanding of potential pathogenic mechanisms.

神经退行性疾病（neurodegenerative diseases）包括阿尔茨海默病（Alzheimer’s disease, AD）、帕金森病等多种类型，可通过机体神经元结构或功能进行性丧失，引发痴呆等认知功能障碍。然而，此类疾病的病因学仍不明确，且血管性痴呆（vascular dementia, VaD）等相对少见的认知障碍疾病的诊断仍存在挑战。本研究开发了一种基于机器学习的方法，可在微小RNA（microRNA, miRNA）表达层面区分正常对照（normal control, NC）、阿尔茨海默病、血管性痴呆、路易体痴呆以及轻度认知障碍。首先，采用Boruta特征选择法去除miRNA表达谱中不必要的miRNA特征，并通过最小冗余最大相关性与蒙特卡洛特征选择对保留的特征集进行排序，得到两份排序后的特征列表。随后，利用增量特征选择方法从这两份特征列表中构建一系列特征子集，并基于包含这些特征子集的样本数据，训练随机森林与PART分类器。基于不同特征数量下分类器的模型性能，本研究确定了最优特征子集与分类器，并从最优PART分类器中提取分类规则。最后，通过近期发表的研究验证了包括hsa-miR-3184-5p、has-miR-6088以及has-miR-4649在内的候选miRNA特征与神经退行性疾病之间的关联，为后续开展miRNA在神经退行性疾病中的相关研究，以实现认知障碍诊断及解析潜在致病机制奠定了基础。