Paper Info
Title
Relating Diseases Based on Disease Module Theory.
Abstract
Understanding disease-disease associations can not only help us gain deeper insights into complex diseases, but also lead to improvements in disease diagnosis, drug repositioning and new drug development. Due to the growing body of high-throughput biological data, a number of methods have been proposed for the computation of similarity among diseases during past decades. Recently, the disease module theory has been presented, which states that disease-related genes or proteins tend to interact with each other in the same neighborhood of protein-protein interaction network. In this study, we propose a new method called ModuleSim to measure associations between diseases by using disease-gene association data and protein-protein interaction network data based on disease module theory. By considering the interactions between disease modules and each module's modularity, ModuleSim outperforms other four popular methods for predicting disease-disease similarity.
Year
DOI
Venue
2017
10.1007/978-3-319-59575-7_3
BIOINFORMATICS RESEARCH AND APPLICATIONS (ISBRA 2017)
Keywords
Field
DocType
Disease-disease association,Disease module,Protein-protein interaction network
Protein protein interaction network,Biological data,Drug repositioning,Disease,Drug development,Computer science,Interaction network,Artificial intelligence,Modularity,Machine learning
Conference
Volume
ISSN
Citations 
10330
0302-9743
0
PageRank 
References 
Authors
0.34
16
7
Name
Order
Citations
PageRank
Peng Ni163.80
Min Li275266.03
Ping Zhong312.37
Guihua Duan475.16
Jianxin Wang52163283.94
Yaohang Li630646.46
Fang-Xiang Wu79727.65