## 4 Discussion  
Model_I was tested with _EX01_ to determine the feasibility of fragmenting the studied miRNA sequences and determining the minimum fragment size of the miRNA sequence while retaining the model accuracy. Dataset_VI was used with this fragmentation model since it has the highest number of sequences samples and, hence, more credible results. Consequently, sequences of Dataset_VI were fragmented arbitrarily by fragmentation size \(f\) = (0.1, 0.2, 0.3, 0.4, and 1), and no further fragmentation between 0.4 and 1 was tested since accuracy sustainability was reached. Accordingly, Dataset_VI was subdivided using Model_I after determining the fragmentation size in each run. Afterward, each sub-dataset DS\({}_{\rm x}\) resulting from the fragmentation is examined with the prediction model, and the MS biomarker detection is reported accordingly for each DS\({}_{\rm x}\). The obtained accuracy scores of Model_I denote that the first sub-dataset DS\({}_{1}\) obtained the highest prediction accuracy, as illustrated in Figure 9 across all fragmentation sizes. This implies that the first sub-dataset of the fragmented files, DS\({}_{1}\), has the most elevated indicative disease biomarkers. Additionally, the obtained results of Ex01 denote that the fragmentation size \(f\) = 0.2 is the minimum sequence fragmentation size that has enough distinctive biomarkers for MS disease detection. The reported scores of this fragmentation size are relative to the scores obtained upon analyzing the entire dataset with no fragmentation (fragmentation size \(f\) = 1). The scores start to diverge upon lowering the fragmentation size to 0.1. This result  
Figure 18: The reported average execution times of: (a) studying Dataset_VI with Model_0; (b) applying Model_I, Model_II, and Model_II to fragmented Dataset_VI (fragmentation size = 0.2). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence Fragmentation Size** & **Number of Dataset_VI Samples after Fragmentation** & **Prediction Accuracy** \\ \hline
**0.1** & 2390 & 0.61 \\ \hline
**0.2** & 1195 & 0.72 \\ \hline
**0.3** & 717 & 0.65 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The results of Ex09. implies that considerable processing time can be saved upon analyzing transcriptomic data using this fragmentation model (22.4% lower execution time, as shall be elaborated on in EX10). Figure 10 illustrates the reported accuracy scores for the tested fragmentation size with Dataset_IV. To ensure that the obtained results of Ex01 are not only related to MS disease, Ex02 was conducted to determine the fragmentation size \(f\) on a different disease dataset. Hence, Model_I was applied on Dataset_I to determine the \(f\) on RA disease. The obtained results were aligned with the findings of Ex01 and confirmed that DS\({}_{1}\) with \(f\) = 0.2 retains the prediction accuracy of the non-fragmented dataset (\(f\) = 1). As illustrated in Figure 11, fragmentation sizes were \(f\) = (0.1, 0.2, 0.3, 0.4, and 1), and no further fragmentation between 0.4 and 1 was tested since accuracy sustainability was reached. Consequently, all following experiments that used the fragmentation model were applied using \(f\) = 0.2 and DS1. Hence, the results of EX01 and EX02 show that with a fragmentation size of \(f\) = 0.2, the first fragmented sub-dataset (DS\({}_{1}\)) can be used for miRNA analysis and biomarker detection using the proposed models instead of running the analysis over the entire dataset being studied, which saves significant computational time as shall be discussed in EX10. EX03 was conducted upon Dataset_I, using Model_I, and detected the biomarkers of RA disease by classifying the entire dataset, with no consideration of the library preparation kit type, into cases (six samples), controls (six samples), and synthetic samples (30 samples).