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By retaining those gene pairs that have a functional association score greater than 500 (the score indicates a strong functional association between the pair of genes), we obtain a lung cancer-specific gene functional association network, which is a binary network, consisting of 7,572 genes and 43,816 edges.
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In this way, an interaction network consisting of binary links becomes a map of weighted or probabilistic links, which enables more powerful network analyses, as has been shown for functional gene networks (e.g Asthana et al., 2004; Lee et al., 2004).
This resulted in a mouse protein interaction network consisting of 20,200 binary protein interactions.
In [17], SC-LDPC codes were used in a decode-and-forward scheme for a network consisting of two sources, one relay and one destination, connected by binary erasure channels.
To represent the global connectivity, we used graph theory analysis to convert the synchronization matrices into binary graphs or networks consisting of 'vertices' (elements) and 'edges' (undirected link between two vertices).
The resulting network consisted of 5,789 unique proteins connected by 14,368 physical binary interactions.
Our network consists of 6 cores.
This network consists of three systems.
A porous network consists of microbubbles.
The citation network consists of 1608 links.
A network consists of nodes and edges.
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