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and are utilized to generate distance transformation images and, respectively.

Local distance vector eliminates the need to search for the nearest few neighbors in every class-specific manifold to generate distance vectors.

Additionally, 3D 1H-15N and 1H-13C NOESY-HSQC spectra with mixing times of 120 ms were collected to generate distance restraints.

PAUP* v4.0b10 [38] was used to generate distance matrices and neighbor-joining (NJ) and maximum-likelihood (ML) phylogenies.

In order to assess the classifier's performance accuracy in identifying the mature miRNA and/or the miRNA:miRNA* duplex, we generate distance distributions showing the percentage of predicted candidates that are located within a specific distance from the respective actual mature miRNAs.

To further investigate the potential role of position <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0011843.e212.PNG" class= inline-graphic"/> triplets in determining the mature miRNA start position, we generate distance distributions of the respective triplets in both 3' and 5' mature samples.

These plots generate distances between samples corresponding to the biological coefficient of variation between the most heterogeneous genes in each sample [ 27].

In the case of dense regions, respectively poor regions, this corresponds to generate distances that are smaller, larger respectively, than x i, j P.

The algorithms have been tested on randomly generated distance matrices F with values distributed between 10−3 and 103.

Using the generated distance matrix, an analysis of molecular variance (AMOVA) was used to determine if the observed differences in microbial diversity between sample groups or extraction methods was significantly different (Schloss2008; Schloss and Handelsman2008).

This tool also generated distance values from the nearest organisms based on the comparison of fatty acid profiles stored in MIDI database.