The phrase "alignment of trees" is correct and usable in written English.
It can be used in contexts related to forestry, landscaping, or environmental studies, where the arrangement or positioning of trees is being discussed.
Example: "The alignment of trees along the pathway creates a beautiful and inviting atmosphere for visitors."
Alternatives: "arrangement of trees" or "positioning of trees".
That revision included people, railways, animals and land alike, even the geometry of farm fields (no acute angles below 70 degrees) and the alignment of trees and shrubs (only on north-south or east-west axes).
If the nested secondary structure is represented as a tree, comparison methods exist for the edit distance between two ordered labeled trees (Zhang and Shasha, 1989) as well as for the alignment of trees (Jiang et al., 1995).
She tried the idea that the alignment of tree, man and sun, meant you had to be on the same side of the tree as the sun, which sort of changes during the course of any day.
Another interesting question is whether polynomial-time methods for tree alignment of unordered trees, such as the constrained tree edit distance (Zhang, 1996), can be used for aligning fragmentation trees: whereas the restrictions imposed by Zhang (1996) have no sensible interpretation in the context of fragmentation trees, quality of results may still be sufficient for certain applications.
Let H S A m (T, S) denote the optimal alignment cost of trees T and S in alignment mode m, where m is one of the following modes: Rooted-Ordered, Rooted-Unordered, Unrooted-Ordered or Unrooted-Unordered.
Whereas efficient, polynomial-time algorithms exist for the alignment of ordered trees, the alignment of unordered trees is computationally hard, namely MAX SNP-hard (Jiang et al., 1995).
Faster algorithms for the computationally demanding alignment of fragmentation trees were presented in [222].
Additional file 4: Figures and alignment of partial trees.
In this article, we have presented three exact algorithms for the alignment of fragmentation trees.
For Orbitrap, the classical DP finished in 5.4 s for the all-against-all alignment of 93 trees, whereas sparse DP required 0.6 s, a 9-fold speed-up.
For MassBank, the classical DP (Section 3) finished in 4.2 s for an all-against-all alignment of 343 trees, whereas sparse DP (Section 4) only required 1.8 s.
