Authors
Kezhi Lu, Kuo Yang, Edouard Niyongabo, Zixin Shu, Jingjing Wang, Kai Chang, Qunsheng Zou, Jiyue Jiang, Caiyan Jia, Baoyan Liu, Xuezhong Zhou
Publication date
2020/7/1
Journal
Journal of Biomedical Informatics
Volume
107
Pages
103482
Publisher
Academic Press
Description
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 …
Total citations
20212022202320244224
Scholar articles
K Lu, K Yang, E Niyongabo, Z Shu, J Wang, K Chang… - Journal of Biomedical Informatics, 2020