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A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification

Overview of attention for article published in BMC Genomics, January 2018
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Title
A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification
Published in
BMC Genomics, January 2018
DOI 10.1186/s12864-017-4332-z
Pubmed ID
Authors

Wei-Feng Guo, Shao-Wu Zhang, Qian-Qian Shi, Cheng-Ming Zhang, Tao Zeng, Luonan Chen

Abstract

The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng . In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 37 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 37 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 24%
Student > Master 6 16%
Researcher 4 11%
Student > Bachelor 2 5%
Student > Postgraduate 2 5%
Other 5 14%
Unknown 9 24%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 19%
Computer Science 6 16%
Engineering 5 14%
Mathematics 3 8%
Agricultural and Biological Sciences 3 8%
Other 4 11%
Unknown 9 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 August 2018.
All research outputs
#19,017,658
of 23,577,761 outputs
Outputs from BMC Genomics
#8,327
of 10,800 outputs
Outputs of similar age
#333,642
of 444,090 outputs
Outputs of similar age from BMC Genomics
#166
of 207 outputs
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