# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 3 Results and Discussion
### Selection and Validation of Key Genes  
We selected the 20 top-ranked genes by LASSO, IG, and ReliefF to find the key common genes identified by the three methods (Figure 3). The intersection of the top 20 genes from each of the three feature selection methods (ReliefF, IG, and LASSO) led to the identification of three common genes, AT5G44050, AT2G47180, and AT1G70700 (Figure 3).  
A random forest algorithm was implemented to identify the performance of this three-gene signature. OOB error was estimated to be 8.7% (Figure 4a). The ROC was plotted by the true positive rate against the false positive rate. Therefore, the primary focus was on AUC to measure classification performance. The AUC of the three-gene set was equal to  
Figure 3: Venn diagram of common overlapping genes for the top 20 ranked genes by the information gain (IG), ReliefF, and LASSO methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & \multicolumn{2}{c}{**Predicted Class**} & \multirow{2}{*}{**Actual**} & \multirow{2}{*}{**Accuracy (\%)**} & \multirow{2}{*}{**TP**} & \multirow{2}{*}{**FP**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Recall**} & \multirow{2}{*}{**F-Measure**} & \multirow{2}{*}{**MCC**} & **ROC** & **Area** & **PRC** \\ \cline{2-2} \cline{6-11}  & **Control** & & & & & & & & & & & Area & Area \\ \hline BayesNet & 152 & 8 & control & 94.93 & 0.949 & 0.051 & 0.949 & 0.949 & 0.898 & 0.993 & 0.993 \\  & 9 & 166 & stress & & & & & & & & \\ Logistic & 156 & 4 & control & 95.52 & 0.955 & 0.043 & 0.956 & 0.955 & 0.955 & 0.911 & 0.976 & 0.965 \\  & 11 & 164 & stress & & & & & & & & \\ Multilayer & 158 & 2 & control & 98.51 & 0.985 & 0.015 & 0.985 & 0.985 & 0.985 & 0.97 & 0.998 & 0.998 \\  & 3 & 172 & stress & & & & & & & & \\ SMO & 159 & 1 & control & 97.61 & 0.976 & 0.022 & 0.977 & 0.976 & 0.976 & 0.953 & 0.977 & 0.965 \\  & 7 & 168 & stress & & & & & & & & \\ Random & 160 & 0 & control & 98.51 & 0.985 & 0.014 & 0.986 & 0.985 & 0.985 & 0.971 & 0.998 & 0.999 \\  & 5 & 170 & stress & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: The confusion matrices and discrimination performance of Arabidopsis on expression levels of 20 selected differentially expressed genes (DEGs) under control and stress conditions based on the ReliefF feature selection algorithm.  
0.921875, indicating that the gene set is excellent in discriminating control samples from those subjected to various types of stresses (Figure 4b) and can be introduced as potential biomarkers for stress tolerance in Arabidopsis owing to their efficient discrimination between control and stress conditions (Figure 4). Further, the mean decrease accuracy and mean decrease gini of each gene in the random forest algorithm were measured (Figure 4c). AT2G47180 seems to have the biggest contribution to the model, followed by AT5G44050 and AT1G7070. The same rank was obtained by the Boruta algorithm, and the contribution of the three-gene signature was confirmed. In comparison, the XGBoost classification model exhibited superior performance with an accuracy of 0.991%, a sensitivity of 0.9876%, and a specificity of 0.9943%. The XGBoost has proven to perform better in terms of efficiency and performance relative to other classifiers [33].