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The mutual cluster method works well in recognizing distinct small size clusters.
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For different communities from each cluster method, we calculated the normalized mutual information among LCBN, SBN and HGNC gene family to compare the three methods.
For example, five glycolysis genes are identified as one mutual cluster, eight out of nine histone genes are found within one mutual cluster.
However, the size of a typical mutual cluster is generally quite small ranging from 2 to 8 with the majority of 2. We cannot use mutual clusters alone to identify any bigger size clusters.
Another problem associated with hybrid clustering is that with an increasing density of gene numbers, some genes will likely occur within the boundary of any mutual cluster, thus making it dificult to find mutual clusters [ 19].
A hierarchical clustering method using mutual information between VSiftVar and VDisGene was used to detect relative closeness patterns of subpopulations in the 1000 Genomes Project data and diseases.
Operations Cell or cluster methods should be adopted by the Organization.
When examining some of the mutual clusters, we find out that those mutual clusters are indeed highly correlated with each other.
Moreover, mutual clustering is sensitive to small data variations which may easily cause gene membership change.
A user can perform a constrained top-down clustering, which inhibits the breaking of any identified mutual clusters.
The key idea is to create mutual clusters comprised of members closer to each other than to any other members.
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