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Integrated network analysis of symptom clusters across disease conditions.

Abstract：

: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.

journal_name

J Biomed Inform

journal_title

Journal of biomedical informatics

authors

Lu K,Yang K,Niyongabo E,Shu Z,Wang J,Chang K,Zou Q,Jiang J,Jia C,Liu B,Zhou X

doi

10.1016/j.jbi.2020.103482

subject

Has Abstract

pub_date

2020-07-01 00:00:00

pages

103482

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(20)30109-X

journal_volume

107

pub_type

杂志文章

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