Topic
Hierarchical closeness
About: Hierarchical closeness is a research topic. Over the lifetime, 3 publications have been published within this topic receiving 12 citations.
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TL;DR: H hierarchical closeness proposed in this study is a novel structural measure to efficiently predict putative disease genes in a directed signaling network and outperforms other structural centrality measures in disease gene prediction.
12 citations
TL;DR: A novel structural metric called hierarchical closeness (HC) entropy was devised and found that it was negatively correlated with 5-year survival rates and it was suggested that cancers of high HC entropy tend to be incurable because their signaling networks are robust to perturbations caused by treatment.
Abstract: Specific molecular signaling networks underlie different cancer types and quantitative analyses on those cancer networks can provide useful information about cancer treatments. Their structural metrics can reveal survivability of cancer patients and be used to identify biomarker genes for early cancer detection. In this study, we devised a novel structural metric called hierarchical closeness (HC) entropy and found that it was negatively correlated with 5-year survival rates. We also made an interesting observation that a network of higher HC entropy was likely to be more robust against mutations. This finding suggested that cancers of high HC entropy tend to be incurable because their signaling networks are robust to perturbations caused by treatment. We also proposed a novel core identification method based on the reachability factor in the HC measure. The cores were permitted to decompose such that the negative relationship between HC entropy and cancer survival rate was consistently conserved in every core level. Interestingly, we observed that many promising biomarker genes for early cancer detection reside in the innermost core of a signaling network. Taken together, the proposed analyses of the hierarchical structure of cancer signaling networks may be useful in developing future novel cancer treatments.
6 citations
TL;DR: Experiments show that comparing with closeness centrality, HCC is a better index in finding most influential vertices and community detection, and a parallel algorithm for HCC computation is presented, by well analyzing the independence between vertices in the computation procedure.
Abstract: It has long been an area of interest to identify important vertices in social networks. Closeness centrality is one of the most popular measures of centrality of vertices. Generally speaking, it measures how a node is close to all other nodes on average. However, closeness centrality measures the centrality from a global view. Consequently, in real-world networks that is normally composed by some communities connected, using closeness centrality may suffer from the flaw that local central vertices within communities are neglected. To resolve this issue, we propose a new centrality measure, Hierarchical Closeness Centrality (HCC), to depict the local centrality of vertices. Experiments show that comparing with closeness centrality, HCC is a better index in finding most influential vertices and community detection. Furthermore, we present a parallel algorithm for HCC computation, by well analyzing the independence between vertices in the computation procedure. Extensive experiments on real-world datesets demonstrate that the parallel algorithm can greatly reduce the computation time compared to trivial algorithms.
2 citations