Paper Info
Title
Investigating Alzheimer'S Disease Candidate Genes Based On Combined Network Using Subnetwork Extraction Algorithms
Abstract
There is increasing need for accurate Alzheimer's disease (AD) related genes prediction to inform study design, but available genes estimates are limited. In this study, the subnetwork extraction algorithms were applied to extract subnetworks and mine candidate genes based on a combined network, which was constructed by integrating the information of protein-protein interactions and gene-gene co-expression network. We obtained seven candidate genes with high possibility during AD progression. The application of subnetwork extraction algorithms based on combined network would provide a new insight into predicting the AD-related genes.
Year
DOI
Venue
2017
10.1007/978-3-319-63312-1_49
INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2017, PT II
Keywords
Field
DocType
Alzheimer's disease, Protein-protein interactions, Gene-gene coexpression, Subnetwork extraction algorithm, Candidate genes
Disease,Candidate gene,Computer science,Algorithm,Artificial intelligence,Subnetwork,Machine learning
Conference
Volume
ISSN
Citations 
10362
0302-9743
0
PageRank 
References 
Authors
0.34
4
6
Name
Order
Citations
PageRank
Xiao-Juan Wang1228.34
Hua Yan244.19
Di Zhang322.75
Le Zhao413.05
Yannan Bin512.72
Junfeng Xia614420.14