Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes. Extracting patterns of disease variable progression for subtypes has tremendous application in medicine, for example, in early prognosis and personalized medical therapy. This work present a novel, data-driven, network-based Trajectory Clustering (TC) algorithm for identifying Parkinson's subtypes based on disease trajectory. Modeling patient-variable interactions as a bipartite network, TC first extracts communities of co-expressing disease variables at different stages of progression. Then, it identifies Parkinson's subtypes by clustering similar patient trajectories that are characterized by severity of disease variables through a multi-layer network. Determination of trajectory similarity accounts for direct overlaps between trajectories as well as second-order similarities, i.e., common overlap with a third set of trajectories. This work clusters trajectories across two types of layers: (a) temporal, and (b) ranges of independent outcome variable (representative of disease severity), both of which yield four distinct subtypes. The former subtypes exhibit differences in progression of disease domains (Cognitive, Mental Health etc.), whereas the latter subtypes exhibit different degrees of progression, i.e., some remain mild, whereas others show significant deterioration after 5 years. The TC approach is validated through statistical analyses and consistency of the identified subtypes with medical literature. This generalizable and robust method can easily be extended to other progressive multi-variate disease datasets, and can effectively assist in targeted subtype-specific treatment in the field of personalized medicine.

中文翻译：

---

多层轨迹聚类：疾病分型的网络算法

许多疾病在临床特征及其进展方面表现出异质性，表明存在疾病亚型。提取亚型的疾病变量进展模式在医学中具有巨大的应用，例如，在早期预后和个性化医学治疗中。这项工作提出了一种新颖的、数据驱动的、基于网络的轨迹聚类 (TC) 算法，用于根据疾病轨迹识别帕金森氏症的亚型。TC 将患者变量交互建模为一个双向网络，首先提取在不同进展阶段共表达疾病变量的社区。然后，它通过通过多层网络对以疾病变量的严重程度为特征的相似患者轨迹进行聚类来识别帕金森的亚型。轨迹相似性的确定说明了轨迹之间的直接重叠以及二阶相似性，即与第三组轨迹的共同重叠。这项工作将轨迹聚集在两种类型的层中：（a）时间和（b）独立结果变量（代表疾病严重程度）的范围，两者都产生四种不同的亚型。前一种亚型在疾病领域（认知、心理健康等）的进展方面表现出差异，而后一种亚型表现出不同程度的进展，即，一些保持轻微，而另一些则在 5 年后表现出显着恶化。TC 方法通过统计分析和确定的亚型与医学文献的一致性得到验证。