Paper Info
Title
A algorithm for identifying disease genes by incorporating the subcellular localization information into the protein-protein interaction networks
Abstract
Disease gene identification is a key step to understand the cellular mechanisms associated with a specific disease. Compared with biological experiments, computational predictions of disease genes are cheaper and more effortless. Many computational methods are used to detect causal genes for diseases on the protein-protein interaction (PPI) networks generated by the high-throughput technology. However, the accuracy of these methods need to be improved due to the false interactions in the PPI data. To deal with the challenge, other methods are proposed via the integration of biological information from different sources with the PPI networks. In this work, a new algorithm AIDG is developed to predict disease genes. First, the weighted PPI networks are built by incorporating the protein subcellular localization information into the human PPI networks. Next, all of disease candidate genes are scored in terms of a iteration function. Finally, they are ranked on descending order of their scores. The top candidates are considered as potential disease genes. The results from the leave-one-out crossing validation (LOOCV) show that AIDG outperforms other similar methods like DADA and ToppNet.
Year
DOI
Venue
2016
10.1109/BIBM.2016.7822537
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
Protein-protein interaction,Subcellular localization,Disease gene
Disease,Gene,Ranking,Candidate gene,Computer science,Protein engineering,Artificial intelligence,Bioinformatics,Disease gene identification,A* search algorithm,Machine learning,Subcellular localization
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-5090-1612-9
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Xiwei Tang100.68
Xiaohua Hu22819314.15