Working as per the method outlined in variable selection methodology, we select four variables, namely, (i) SPR-at-lag-one, (ii) Maximum Temperature, (iii) Minimum Humidity, and, (iv) Population, as independent variables.
Following this methodology, we select 47 groups of similar earthquakes.
To present our methodology, we selected the Halilrud basin and Jiroft city in southeastern Iran as an example of hazardous regions.
In terms of methodology, we selected intragenic markers and used laser capture microdissection for our LOH analysis to provide an unbiased estimate of the genetic status of these genes in cancer cells, and our careful examination of protein expression based on serial sections excluded the possibility of an effect of intratumour heterogeneity.
The following limitations apply to the methodology that we selected for this study.
To illustrate the methodology, we have selected a concrete example, where we perform all the necessary steps for the procedure described above.
To further validate the accuracy of our methodology, we randomly selected 10 substrates which were shown to be ubiquitylated on the protein microarrays (by both rabbit reticulocyte lysate and HeLa S-100 fraction) but whose modification was not reported in the literature and attempted to verify whether they were substrates of ubiquitylation in vivo.
- Using probability proportional to size sampling (PPS) methodology, we randomly selected 30 enumeration areas (EAs) representing all 14 districts in Sierra Leone, with support from the National Statistics Office using population data from the 2004 national census.
Using this sampling methodology, we initially selected 64% (n=631) of hospitals in the Midwest region for the comparison group, including all large teaching, large non-teaching, and small teaching hospitals in the region.
The methodology we have selected here, based on fusion protein expression cloning, enabled us to distinguish functional immunoglobulin genes that produced protein products of predicted size from aberrant immunoglobulin genes.
The reason why we select Deep Learning methodology rather than data mining is that in data mining, identifying features and selecting the best of those features is the most challenging task to undertake especially in a Big Data.
