However, since the data have random behavior, it is difficult to apply statistical approaches with apriori and deterministic parameters.
Thus researchers are encouraged to apply statistical approaches such as 'response surface methodology' (RSM), which provide a great amount of information based on only a small number of experiments [21, 22].
To identify the loci most robustly associated with invasive disease, we utilized 3 distinct, commonly applied statistical approaches to identify those gene loci (from each of the three statistical approaches and across the 2 series) that overlapped in differentiating invasive and non-invasive disease.
The overarching aim of this study was to apply statistical approaches to interrogate causal relationships using genomic, metabolomic, and circulating lipid biomarker measures as an exemplar model.
Hence, we applied statistical approaches that allow control for the variation between species and between lineages (see below), with the result that the tests would permit implications independent of particular species or MHC lineage.
This results in a push for public health initiatives to have representation according to administrative divisions such as a surveillance site in each province or to apply statistical approaches for calculation of the number of sentinel sites.
The first platform applies statistical approaches based on the syntactic structures of the text to disambiguate the entities recognized by GATE, whereas the second one expands the recognized annotations in the text with a graph, which facilitates the access and navigation of the learning contents.
The widely applied statistical approaches to infer relatedness among individuals can be classified into two categories: one involves the explicit pedigree reconstruction among all individuals in the sample; and the other is based on the best pairwise relationship between two individuals at a time based on either relatedness estimation [ 13- 15] or likelihood techniques [ 16, 17].
We apply statistical approach to assess the performance of the developed MLP model.
An overview of the applied statistical approach is presented in Figure 1.
One reason for this might be that most (Q SAR models are the results of applying statistical machine learning approaches to chemical datasets and the resulting models are sometimes opaque and it is commonly not an easy task to extract the reasoning behind a prediction.
