Sentence examples for are typically explored from inspiring English sources

These types of relationships are typically explored using some type of similarity measure, e.g. Pearson's correlation coefficient (PCC), to quantify the association between two genes in the genome.

To date, individual differences in RT tasks are typically explored by either the computation of different RT indices for separate groups (e.g., Rinck & Becker, 2007; Wiers et al., 2009) or the use of correlations between some RT index and particular individual differences factors (Klein et al., 2011).

This regime is typically explored by pulling experiments.

High dimensional data, such as is generated by GCxGC MS, is typically explored using PCA; PC loadings are one method to extract which compounds show the greatest variation, and therefore may be having greatest influence in distinguishing groups.

The impact of the adherence parameter on model-simulated outcomes is typically explored via sensitivity analyses in which the level of adherence is varied systematically.

This seems like a very important and novel finding as sequences have not been typically explored there (at least in the monkey neurophysiological literature).

Although protein aggregation, a key feature of human neurodegenerative diseases, has been typically explored in vivo at the single-cell level using cells in culture, there is now increasing evidence that proteotoxicity has a non-cell-autonomous component and is communicated between cells and tissues in a multicellular organism.

They are typically all about exploring, harvesting some form of resource, building an army and destroying the enemy.

Grounded and phenomenological approaches are typically used to explore and elaborate on the richness of individuals' perspectives, experiences, and goals (cf., [ 18, 19]).

Multiple types and sources of data are typically used to explore, describe, and explain the phenomenon of interest within each case [ 74– 76].

A substantial literature exists on the estimation of k, but most attention has focused on datasets that are not highly overdispersed (i.e., those with k≥1), and the accuracy of confidence intervals estimated for k is typically not explored.