Paper Info
Title
MorbidGCN: prediction of multimorbidity with a graph convolutional network based on integration of population phenotypes and disease network
Abstract
Exploring multimorbidity relationships among diseases is of great importance for understanding their shared mechanisms, precise diagnosis and treatment. However, the landscape of multimorbidities is still far from complete due to the complex nature of multimorbidity. Although various types of biological data, such as biomolecules and clinical symptoms, have been used to identify multimorbidities, the population phenotype information (e.g. physical activity and diet) remains less explored for multimorbidity. Here, we present a graph convolutional network (GCN) model, named MorbidGCN, for multimorbidity prediction by integrating population phenotypes and disease network. Specifically, MorbidGCN treats the multimorbidity prediction as a missing link prediction problem in the disease network, where a novel feature selection method is embedded to select important phenotypes. Benchmarking results on two large-scale multimorbidity data sets, i.e. the UK Biobank (UKB) and Human Disease Network (HuDiNe) data sets, demonstrate that MorbidGCN outperforms other competitive methods. With MorbidGCN, 9742 and 14 010 novel multimorbidities are identified in the UKB and HuDiNe data sets, respectively. Moreover, we notice that the selected phenotypes that are generally differentially distributed between multimorbidity patients and single-disease patients can help interpret multimorbidities and show potential for prognosis of multimorbidities.
Year
DOI
Venue
2022
10.1093/bib/bbac255
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
multimorbidity, graph convolutional network, population phenotype, disease network
Journal
23
Issue
ISSN
Citations 
4
1467-5463
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Guiying Dong100.34
Zi-Chao Zhang200.34
Jianfeng Feng364688.67
Xing-Ming Zhao414615.55