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research
An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links
Authors
A Madi
A Madi
+29 more
AG Randolph
Chad Kimmel
D Nitsch
EA Adie
EA Adie
Eshel Ben-Jacob
G Gonzalez
G Ivan
J Chen
JM Kleinberg
JY Chen
JZ Wang
KG Becker
KR Brown
KR Brown
L Franke
M Ashburner
M Oti
MD McDowall
MS Scott
P Kauppi
S Aerts
S Havlin
S Kohler
Shyam Visweswaran
U Ala
X Wu
Y Chen
Y Moreau
Publication date
1 January 2012
Publisher
'Public Library of Science (PLoS)'
Doi
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on
PubMed
Abstract
Background: Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods - which utilize a knowledge network derived from biological knowledge - have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes. Results: We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone. Conclusions: The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization. © 2013 Kimmel, Visweswaran
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