Sentence examples for dissimilarities in data from inspiring English sources

This way, the multidimensional scaling techniques are used in information visualization for exploring similarities or dissimilarities in data.

Dissimilarities in data sources, clinical care settings, data analysis techniques, and patient characteristics may explain some of the differences in the treatment discontinuation estimates.

Multidimensional scaling is a multivariate statistical technique often used to visualize information for exploring similarities or dissimilarities in data [ 27], and it was utilized here to discern and explore the similarities and dissimilarities among the clinical features.

To confirm that the 10-gene panel RT-MLPA could distinguish samples according to their ATM/p53 mutational status, we performed a multidimensional scaling analysis, a statistical method for exploring similarities or dissimilarities in data.

It is tempting to suggest that the dissimilarity in data between Ma et al. [20] and our study, has its origins in technical factors.

They analysed the data using multidimensional scaling, which enables visualisation of similarities or dissimilarities in the data by locating the more similar stimuli closer to one another on a plot.

In order to explore similarities or dissimilarities in the data, phylogenetic trees were generated with maximum parsimony in PAUP 4.0 and maximum likelihood in PhyML with HKY85+G.

Despite the dissimilarities in our aggrecan and COL2A1 data relative to previous reports, it was interesting that the expression of Sox9 and COL2A1 correlated linearly in mono-cultures of MSCs and MCO and in co-cultures of MCO/MSCs but inversely in mono-cultures of MCI.

In Eq. (14), the left-hand side matrix shows the dissimilarities among data points and the right vector in the Eq. (14) describes the dissimilarities between the estimation point and the data points.

The Euclidean distance metric is often used to calculate dissimilarities for data that can be represented as points in a multidimensional metric space.

Data clustering, one of the most important techniques in data mining, aims to group unlabeled data into different groups on the basis of similarities and dissimilarities between the data elements.