DocumentCode :
3714398
Title :
Joint inference of tissue-specific networks with a scale free topology
Author :
Somaye Hashemifar;Behnam Neyshabur; Jinbo Xu
Author_Institution :
Toyota Technological Institute at Chicago, IL 60637, United States of America
fYear :
2015
Firstpage :
290
Lastpage :
294
Abstract :
High-throughput experimental techniques have produced an enormous number of gene expression profiles for various tissues of the human body. Tissue-specificity is a key component in reflecting the potentially different roles of proteins in diverse cell lineages. One way of understanding the tissue specificity is by reconstructing the tissue-specific co-expression networks (CENs) to analyze the correlation between genes. A few methods have been developed for estimating CENs, but it still remains challenging in terms of both accuracy and efficiency. In this paper we propose a new method, JointNet, for predicting tissue-specific co-expression networks. JointNet is exploiting the observation that, functionally related tissues have similar expression patterns and thus, similar networks. It uses different node penalties for hubs and non-hub nodes to accurately estimate the scale-free networks. Our experimental results show that the resulting tissue-specific CENs are accurate and that our method outperforms the current state of the art.
Keywords :
"Bioinformatics","Proteins","Correlation","Art","Yttrium","Prediction algorithms"
Publisher :
ieee
Conference_Titel :
Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/BIBM.2015.7359696
Filename :
7359696
Link To Document :
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