DocumentCode
518474
Title
Notice of Retraction
Application of eigenvector centrality in metabolic networks
Author
De-wu Ding ; Xiao-qing He
Author_Institution
Dept. of Math. & Comput. Sci., Chizhou Coll., Chizhou, China
Volume
1
fYear
2010
fDate
16-18 April 2010
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Topological analysis methods have been developed to study the fundamental organizational principles of large-scale metabolic networks, which would be operated maintain life in human and other organisms. Among a lot of topological analysis methods, a number of recent studies have focused on the centrality methods, which could be help to seek for important metabolites in metabolic networks, and would greatly aid drug target identification and therapy design. In this paper, we engage eigenvector centrality method to study metabolic networks of 20 organisms from different species, we analyze the first 10 important metabolites in these 20 metabolic networks and discuss their biological signification.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Topological analysis methods have been developed to study the fundamental organizational principles of large-scale metabolic networks, which would be operated maintain life in human and other organisms. Among a lot of topological analysis methods, a number of recent studies have focused on the centrality methods, which could be help to seek for important metabolites in metabolic networks, and would greatly aid drug target identification and therapy design. In this paper, we engage eigenvector centrality method to study metabolic networks of 20 organisms from different species, we analyze the first 10 important metabolites in these 20 metabolic networks and discuss their biological signification.
Keywords
biology computing; eigenvalues and eigenfunctions; gene therapy; genomics; molecular biophysics; network topology; object detection; eigenvector centrality method; genome sequencing; metabolic networks; target identification; therapy design; topological analysis methods; Application software; Biochemistry; Drugs; Educational institutions; Helium; Large-scale systems; Mathematics; Medical treatment; Organisms; Statistics; eigenvector centrality; metabolic network; topological analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
Conference_Location
Chengdu
Print_ISBN
978-1-4244-6347-3
Type
conf
DOI
10.1109/ICCET.2010.5486281
Filename
5486281
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