## 4 Data Preparation and Results
### Results and Discussions  
After we categorized and prepared the data from the TCGA repository, we checked the data points and found out that the data variation was too large, therefore it needed to be normalized. For this purpose, a logarithmic kernel was first applied to reduce the variance of the data points, then a normalization procedure was devised to ensure that the data points obeyed zero mean and unit variance. Once the data was ready, we fed the labeled data files to a feature selection algorithm. We hypothesized that if all the miRNAs were used for cancer subtype classification, it would produce the best possible outcomes. We tested this hypothesis with an experiment as follows. First, we used all the data points without dimension reduction, and then used the NCA algorithm without regularization for five kidney cancer subtype classification. Indeed, we found out that the former in general produced better results. However, one would not know which miRNAs are more important for the classification if all the miRNAs are used. It is therefore important to select the miRNAs that have high discriminative capability. To find the most discriminative miRNAs, the value of \(\lambda\) in the NCA algorithm needs to be tuned. For this, a five-fold cross-validation test was performed. For each fold, randomly selected 80% of the data is used as a training set, and the remaining 20% of the data as a test set. To produce reproducible results, the procedure needs to be repeated 10 times. Figure 4 shows the the average loss values of the five-fold validation verses \(\lambda\) values. Using the tuned \(\lambda\) value, NCA is applied to find the maximum weighted features, i.e., the most effective miRNAs that have the greatest discriminative power among the kidney cancer subtypes. Figure 5 shows the selected miRNAs according to NCA features weight value. Having a higher feature weight corresponds with better discriminative power for subtype classification. Table 2 shows the values of the selected Feature weight with corresponding miRNA name and index, where the indices of the miRNAs as appeared in the kidney cancer Quantified RNA sequence files in TCGA. Figure 3: Data preparation for kidney cancer subtype classification. In the classification phase, we adopted the LSTM network algorithm with two LSTM layers. The first hidden layer had 500 neurons and the second one had 250 neurons. The hardware platform was NVIDIA TITAN X GPU. Ten runs of randomized five-fold validation was adopted for data analysis. The procedure followed largely the Data Analysis Protocol (DAP), which was defined by the US-FDA MAQC-II initiative [31]. Both the selected miRNA subset and the complete miRNA dataset were trained using the LSTM network. In each five-fold validation, a procedure of randomizing all the data points was performed for both the training and testing sets, whose average values were used to compute the total confusion matrices, which are given in Figures 6 and 7, where indices of the classes are as follows: class 1 = WT, class 2 = KICH, class 3 KIRC, class 4 = KIRP, and class 5 = RT. Figure 4: The average loss values versus lambda (\(\lambda\)) values. Figure 5: The selected features/miRNAs according to NCA feature weights. A higher weight value indicates better discrimination power. A threshold of 0.02 was applied to reduce the number of miRNAs to 35. \begin{table}
\begin{tabular}{r l l} \hline
**miRNA Index** & **miRNA Name** & **Feature Weight** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 2: Selected miRNA names and index with corresponding feature weights. Figure 6: Classification results using all of the 1627 miRNAs as features. It can be observed in this experimental study that using only the 35 selected miRNAs as features performs competitively with using all the available miRNAs. One issue with the results presented in Figures 6 and 7 was that the dataset was not balanced.