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Integrated network analysis of symptom clusters across disease conditions.
Journal of Biomedical informatics ( IF 4.5 ) Pub Date : 2020-06-11 , DOI: 10.1016/j.jbi.2020.103482
Kezhi Lu 1 , Kuo Yang 1 , Edouard Niyongabo 1 , Zixin Shu 1 , Jingjing Wang 1 , Kai Chang 1 , Qunsheng Zou 1 , Jiyue Jiang 1 , Caiyan Jia 1 , Baoyan Liu 2 , Xuezhong Zhou 3
Affiliation  

Identifying the symptom clusters (two or more related symptoms) with shared underlying molecular mechanisms has been a vital analysis task to promote the symptom science and precision health. Related studies have applied the clustering algorithms (e.g. k-means, latent class model) to detect the symptom clusters mostly from various kinds of clinical data. In addition, they focused on identifying the symptom clusters (SCs) for a specific disease, which also mainly concerned with the clinical regularities for symptom management. Here, we utilized a network-based clustering algorithm (i.e., BigCLAM) to obtain 208 typical SCs across disease conditions on a large-scale symptom network derived from integrated high-quality disease-symptom associations. Furthermore, we evaluated the underlying shared molecular mechanisms for SCs, i.e., shared genes, protein–protein interaction (PPI) and gene functional annotations using integrated networks and similarity measures. We found that the symptoms in the same SCs tend to share a higher degree of genes, PPIs and have higher functional homogeneities. In addition, we found that most SCs have related symptoms with shared underlying molecular mechanisms (e.g. enriched pathways) across different disease conditions. Our work demonstrated that the integrated network analysis method could be used for identifying robust SCs and investigate the molecular mechanisms of these SCs, which would be valuable for symptom science and precision health.



中文翻译:

跨疾病状况的症状群的综合网络分析。

识别具有共同潜在分子机制的症状群(两个或更多个相关症状)已成为促进症状科学和精准健康的重要分析任务。相关研究已应用聚类算法(例如k均值,潜在类模型)来主要从各种临床数据中检测症状聚类。此外,他们专注于确定特定疾病的症状群(SCs),这也主要涉及症状管理的临床规律。在这里,我们利用基于网络的聚类算法(即BigCLAM)在从综合的高质量疾病-症状关联获得的大规模症状网络上获得了跨越疾病状况的208个典型SC。此外,我们评估了SC的潜在共享分子机制,即共享基因,使用集成网络和相似性度量进行蛋白质间相互作用(PPI)和基因功能注释。我们发现,同一SC中的症状倾向于共享更高程度的基因,PPI,并且具有更高的功能同质性。此外,我们发现大多数SCs在不同疾病状况下具有相关的症状,具有共同的潜在分子机制(例如,丰富的途径)。我们的工作表明,综合网络分析方法可用于识别健壮的SC,并研究这些SC的分子机制,这对症状科学和精确健康具有重要价值。PPI具有更高的功能同质性。此外,我们发现大多数SCs在不同疾病状况下具有相关的症状,具有共同的潜在分子机制(例如,丰富的途径)。我们的工作表明,综合网络分析方法可用于识别健壮的SC,并研究这些SC的分子机制,这对症状科学和精确健康具有重要价值。PPI具有更高的功能同质性。此外,我们发现大多数SCs在不同疾病状况下具有相关的症状,具有共同的潜在分子机制(例如,丰富的途径)。我们的工作表明,综合网络分析方法可用于识别健壮的SC,并研究这些SC的分子机制,这对症状科学和精确健康具有重要价值。

更新日期:2020-06-23
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