Paper Info
Title
Iteratively Collective Prediction Of Disease-Gene Associations Through The Incomplete Network
Abstract
The prediction of links between genes and disease is still one of the biggest challenges in the field of human health. Almost all state-of-the-art studies on the prediction of gene-disease links focuson a single pair of links, ignoring the associations and interactions among different types of links. Moreover, the biological information networks are usually incomplete. In this paper, we study the similarity measure to be used on two different types of nodes, based on the metapaths between them (WSRM). Then an iterative self-updating approach for link prediction using heterogeneous information network is proposed to fit the incompletion of the network (ISL), which is a semi-supervised learning formula. Using the biological integrated network constructed from OMIM and HumanNet dataset (30,896 nodes and 1,200,166 edges) we applied our framework. The area under the receiver operating characteristic is 0.941, indicating that our approach significantly outperforms the state-of-the-art gene-disease link prediction approaches. Moreover, the sensitivity analysis signifies that our approach is robust. Consequently, our proposed framework demonstrates an efficient and accurate approach for link prediction between genes and diseases. In addition, during iteration, the accuracy of the result gradually increases. The example dataset and the implementation of our approach is avaliable at https://github.com/xymeng16/ISL.
Year
DOI
Venue
2017
10.1109/BIBM.2017.8217854
2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
Keywords
DocType
ISSN
Iterative link prediction, Data Integration, Metapath, Path Count, Random walk, Semi-supervised learning
Conference
2156-1125
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Xiangyi Meng100.34
quan zou255867.61
Alfonso Rodríguez-Patón343551.44
Xiangxiang Zeng458950.79