Sentence examples for most alignment methods from inspiring English sources

However, in most alignment methods only the sequence data or network topology are used to align the input networks despite the availability of other sources of information such as function annotation or gene expression.

Sdev and Smod results within each similarity range are consistent across most alignment methods (Fig. 2), pointing to the inherent ease or difficulty of aligning particular domains.

However, most alignment methods are too slow to perform reference-free classification of a large number of (e.g. tens of thousands) of subtomograms.

However, most alignment methods do not integrate sequence and structural information into one measure of similarity or describe the similarity at the level of individual residues.

Currently, most alignment methods are based on maximizing the overlap similarity of two subtomograms through exhaustive search over all rigid transforms (rotation and translation) of one subtomogram with respect to the other (e.g. Amat et al., 2010; Förster et al., 2008).

Most anchored alignment methods assume that the input sequences are free from genomic rearrangement.

Significant improvements can be observed for both simple (GOTOH) and the most sensitive alignment methods (HHALIGN).

Thus, most graph alignment methods resort to heuristic solutions to reduce the cost of computation.

- Missing references concerning the available PPI datasets, the most important alignment methods and the importance of using network alignment were added.

Although aligning two sequences takes time proportional to the product of their lengths, most alignment-free methods run in linear time.

Most alignment-free methods are based on word frequencies.