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After the network construction, we employ our Dense and Enriched Subgraph Enumeration (DENSE) algorithm [ 90] to enumerate "dense and enriched" subgraphs in each network.
In this paper, we describe a theoretically sound and fast method called the Dense ENriched Subgraph Enumeration (DENSE) algorithm that capitalizes on the availability of any "prior knowledge" about the proteins involved in a particular process and identifies overlapping sets of functionally associated proteins from an organismal network that are enriched with the given knowledge.
[For more details on the DENSE algorithm and the software, please, refer to paper [ 90]].
Here, we use the phenotype-determining components generated by Spice as the query set for the DENSE algorithm.
Of these clusters, the DENSE algorithm identified 158 as containing proteins involved in a sugar phosphotransferase system (PTS).
In this regard, the DENSE algorithm was able to predict the association of this important enzyme when pyruvate lyase was given as a hydrogen-related knowledge prior enzyme.
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For the dense algorithms (2 and 4), three scales were used for estimating the derivatives: σ∈{1.0,2.0,4.0}.
On mammograms whose tissue type is extremely dense the algorithm that gives better results is the algorithm based on β-Spline contrast enhancement.
In Section 3 we introduce a new dense matching algorithm for human motion sequences synchronization.
In this paper, a novel dense matching algorithm for human motion sequences synchronization has been proposed.
A high resolution dense matching algorithm is presented for non-rigid image feature matching in the paper.
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