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A clustering based mate selection for evolutionary optimization

1. Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University Xi'an 710071, China;
2. School of Computer Science, University of Birmingham Birmingham, U. K

Network robustness stands for the capability of networks in resisting failures or attacks. Many robustness measures have been proposed to evaluate the robustness of various types of networks, such as small-world and scale-free networks. However, the robustness of biological networks is different for their special structures related to the unique functionality. Cancer signaling networks which show the information transformation of cancers in molecular level always appear with robust complex structures which mean information exchange in the networks do not depend on skimp pathways in which resulting the low rate of cure, high rate of recurrence and especially, the short time in survivability caused by constantly destruction of cancer. So a network metric that shows significant changes when one node is removed, and further to correlate that metric with survival probabilities for patients who underwent cancer chemotherapy is meaningful. Therefore, in this paper, the relationship between 14 typical cancer signaling networks robustness and those cancers patient survivability are studied. Several widely used robustness measures are included, and we find that the natural connectivity, in which the redundant circles are satisfied with the need of information exchange of cancer signaling networks, is negatively correlated to cancer patient survivability. Furthermore, the top three affected nodes measured by natural connectivity are obtained and the analysis on these nodes degree, closeness centrality and betweenness centrality are followed. The result shows that the node found are important so we believe that natural connectivity will be a great help to cancer treatment.
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Keywords Network robustness; robustness measure; cancer signaling network; cancer patient survivability

Citation: Mingxing Zhou, Jing Liu, Shuai Wang, Shan He. A clustering based mate selection for evolutionary optimization. Big Data and Information Analytics, 2017, 2(1): 87-96. doi: 10.3934/bdia.2017011


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Copyright Info: 2017, Jing Liu, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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