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Discover LudwigThe phrase "alignment algorithms for" is correct and usable in written English.
It can be used in contexts related to computer science, data analysis, or any field where algorithms are applied to achieve alignment in data or processes.
Example: "The research focuses on developing alignment algorithms for optimizing machine learning models."
Alternatives: "alignment methods for" or "alignment techniques for".
Exact(13)
The progressive principle has dominated multiple alignment algorithms for nearly 20 years.
MUSCLE was chosen from among many different sequence alignment algorithms for development of the SCOPPI database.
Also, we thank the authors of the different alignment algorithms for making their programs available.
Second, our method does not rely on sequence alignment algorithms for assessment of conservation and functional specificity.
BAliBASE is arguably the most widely used benchmark for multiple sequence alignment, and it has been utilized by most multiple sequence alignment algorithms for assessing their performance.
We used different alignment algorithms for these 2 data sets because in each case one program gave a higher quality alignment than the other.
Similar(47)
Valouev et al. [ 7] have developed an alignment algorithm for both finding overlaps between two optical maps and aligning an optical map to a reference map.
Table 1 Errors obtained by the coarse alignment algorithm for ten different objects Reduced point cloud 1 Reduced point cloud 2 Rot.
The results illustrate the importance of the alignment algorithm for the evaluation of molecular similarity in a given set of molecules and show that the degree of similarity depends dramatically on the similarity measure used and the space in which the similarity is computed.
Specifically, we used the local gapped alignment algorithm for all alignments, keeping the default parameters for mismatch and deletion costs.
Given a group of species, we used our graph alignment algorithm for pair-wise comparisons and generated a distance matrix (distance score = 1 - similarity score).
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