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We term the location of a "cut" as a "cutting" node.
Each cutting node has only one ancestral cutting node neighbor.
The dissimilarity score for cutting node j and its ancestral cutting node neighbor i, i.e. anc(j), at site m is defined as: and are the reconstructed probabilities of amino acid A at cutting node j and its ancestral cutting node neighbor i anc(j)), respectively.
A dissimilarity score between any neighboring cutting node pair is calculated.
The reconstructed probability vector of a cutting node reflects the amino acid usages of the sub-tree under it.
The definition of a neighboring cutting node pair (i, j) is: 1. i ∈ { L K } 2. j ∈ { L K +1} 3. Node i is an ancestor of node j (all points on the path from j to root node are ancestors of node j), so that the distance between i and j is exactly d r /5.
Similar(54)
We use { L K } to represent the set of cutting nodes for layer L K, K = 0,1,5.
Let, K = 1,...,5 (22) Here, is the average dissimilarity score for layer K. N K is the number of cutting nodes in layer K.
The marginal reconstruction method was used to reconstruct amino acid probability vectors for all the cutting nodes.
Based on a tree partition and the reconstructions at the cutting nodes (details see Methods), we have developed a measure called specificity score (equation (27)).
We attempted to reduce potential sources of inconsistency affecting the indirect evidence (that is, obsolete treatments, cutting balloon node) by restricting our analysis to the smallest network of plain balloons, drug coated balloons, and drug eluting stents.
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