# A Novel Machine-Learning Approach to Predict Stress-Responsive Genes in Arabidopsis
## 2 Materials and Methods
### Discrimination Analysis  
Discrimination analysis was performed using the WEKA software to assess the ability of the selected genes to discriminate between samples under stress and control class [21]. The gene expression matrix associated with the 20 selected genes was used to construct classification models, with each row representing 1 of the 207 samples.  
A preliminary screening was conducted using various methods to identify the most effective classifiers for discriminating between the control and stress classes. Ultimately, the BayesNet, logistic, multilayer perceptron, sequential minimal optimization (SMO), and random forest classifiers were selected for the discrimination analysis. A detailed discussion on the principle and operation of these classifiers can be found elsewhere [21]. The parameters of different classifiers are provided in Table 1.  
The discrimination analysis was conducted using a 10-fold cross-validation approach. The performance of the classifiers was evaluated using various metrics, including confusion matrices, TP (true positive) and FP (false positive) rate values, precision, F-measure, ROC area, and precision-recall (PRC) Area, and Matthews correlation coefficient (MCC). Equations (6)-(13) indicate the equations of the above-mentioned evaluation metrics.  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{FP}+\text{TN}+\text{ FN})\times 100 \tag{6}\]  
\[\text{TPRate}=\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{7}\]  
\[\text{FPRate}=\text{FP}/(\text{FP}+\text{TN}) \tag{8}\]  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{9}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{10}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Classifier** & **Parameter Adjustment** \\ \hline BayesNet & debug: False; estimator: SimpleEstimator; \\  & searchAlgorithms: K2; useADTree: False \\ Logistic & debug: False; maxItés: \(-1\); ridge: \(10^{-8}\) \\  & debug: False; hiddenLayers: a; learningRate: 0.3; momentum: 0.2; \\ Multilayer Perceptron & normalizeNumericClass: True; NominalToBinaryFilter: True; \\  & normalizeAttributes: True; reset: True; seed: 0; \\  & trainingTime: 500; validationThreshold: 20 \\  & buildLogisticModels: False; c 1.0; checksTurnedOff: False; debug: False; \\ SMO & epsilon: \(10^{-12}\); filterType: Normalize training data; kernel: polyKernel, \\  & numFolds: \(-1\); randomSeed: 1; tolerance Parameter: 0.0010 \\ Random Forest & debug: False; maxDepth: 0; numFeatures: 0; numTrees: 10; seed: 1 \\ XGBoost & max\_depth = 2, eta = 1, nround = 2, set.seed = 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of different classifiers for the discrimination of Arabidopsis based on gene expression levels under control and stress conditions.  
\[\text{F1-Measure}=2\times((\text{Precision}\times\text{Recall})/(\text{Precision}+ \text{Recall})) \tag{11}\]  
ROC Area = Area under TP Rate vs. FP Rate curve (12)  
\[\text{PRC Area}=\text{Area under Precision vs. Recall curve} \tag{13}\]  
\[\text{MCC}=\frac{(\text{TP}\times\text{TN})-(\text{FP}\times\text{FN})}{\sqrt {((\text{TP}+\text{FP})\times(\text{TP}+\text{FN})\times(\text{TN}+\text{FP}) \times(\text{TN}+\text{FN}))}} \tag{14}\]  
where TN represents true negative and FN indicates false negative.