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Other aspects of the merge score are described in Yip, et al. (2004) [ 13].
We propose the following merge score to handle overlapping clusters and to improve general clustering performance.
To achieve this, HARP uses a merge score to rate how well two clusters can be merged.
Nevertheless, such merge score have been developed for disjoint clustering only and bias exists in the merge of unequal-sized clusters.
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If there is a total of n clusters, then there are nC2 (n choose 2) merge scores that need to be computed.
Additionally, clusters overlapping with each other may be dependent on each other, in which case merging between them is not necessary and the computation of their merge scores adds unnecessary computational burden.
To reduce computational complexity, the authors proposed the use of individual statistics (e.g. means and variances) of the n separate clusters to compute the merge scores instead of using statistics of the nC2 potential clusters.
The kernelized merging score λ K (t) and collapsing score ϕ K (t) for the t-th snapshot (K denotes kernelized) are (3) (4) Although the same clustering is used across all T time points, the scores will differ when proteins (or interactions) are present in one time point and absent in another.
A decade ago O.J. and his team sold off or merged scores of L'Oréal's disparate businesses to focus on a dozen brands in hair care, skin care, makeup and perfumes.
Each merged scoring matrix was analysed using JoinMap version 4.0 [ 48].
Merge scores correspond to dendrogram heights.
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