Sentence examples for more complex datasets from inspiring English sources

To rule out that the above observations are not specific to the proof-of-concept dataset, we performed the same analysis on the larger, more complex datasets, i.e. WOMBAT and MLSMR.

However, for more complex datasets, like the ETHZ [28], DSaM is much faster (up to about eight times) than the Kovesi implementation, since these datasets, contain a lot of points and Kovesi implementation has to check them all, one by one.

Experiments have been divided in three main groups: i) preliminary tests, aimed at running DAN on few and relatively simple synthetic datasets, ii) proper tests, aimed at running DAN on more complex datasets, and iii) comparisons, aimed at assessing the behavior of DAN with reference to k−Means and spectral clustering.

While there are other gene expression prioritization tools and other prioritization approaches (e.g. via interactome data and literature based) that are also successful, the data suggest that UGET is a robust tool especially when the genes and biological processes are better defined in rare or more complex datasets.

Given more complex datasets, a step-wise approach is necessary to analyze and identify predictive variables.

In contrast, more complex datasets analyzed using ML resulted in an improvement in branch length estimation over simple simulated data.

This indicated that the default FPC parameters and/or data filtering were not stringent enough to build a robust physical map for this more complex dataset.

It is also easy to isolate specific types of genes or organisms from a more complex dataset, allowing the user to answer specific questions.

Sequence logos of these clusters are shown in Figure 2. To investigate the tool's performance on a more complex dataset, a phage display experiment examining three monocolonal antibodies (CHIP 3.1, EEV1‐2.1 and DO.1) was performed.

To asses our approach not only in terms of the improved knowledge obtained from the joint versus separate analysis, but also in terms of the relevance of the information carried by the latent features, we applied the method to a more complex dataset that is a large high-grade serous ovarian adenocarcinomas dataset (HGS-OvCa)[ 4].

However, the added cost of repeated mapping through a series of assembly steps limited the expected efficiency advantage of PRICE to cases where the initial design assumptions hold: when an investigator is interested in a particular species that comprises a small fraction of a much larger, more complex dataset, and that subset of data cannot be reliably identified ahead of time.