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Phase two would focus on shorter answers, and phase three on charts, graphs, representations of math and logic.
This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data.
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The graph representation performs better than standard graph representations for those cases when the rules of geometric locality and uniform element distribution around nodes are violated.
We consider the problem of identifying optimal sparse graph representations of dense consensus networks.
RAG was first reported in 2004 for cataloging RNA secondary structure motifs using graph representations.
When using graph representations, problems of special interest often imply searching.
Figure 5 Graph representations.
Then, we visualized the values as graph representations.
Consensus models have been developed using structure-activity similarity maps and related graph representations.
Much of the recent development in the graph structure of reaction systems has focused on either adapting analysis techniques to complex and informative graph representations [6] [9], or to find simple graph representations encoding as much relevant information as possible [10] [12].
Genes of which the proteins interact via a domain-domain interaction were linked in the corresponding graph representations.
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