## 2 Feature Selection and Preprocessing Process
### Preprocessing Process  
The prediction results after directly calculating all the above feature values for training are not ideal, so we use the sliding window to continuously intercept the area of the window length, calculate the five selected features, and assign them to all residues at the corresponding positions.  
Given a protein sequence of length \(N\), select a sliding window of length \(L\) (\(L<N\)), and add \(N/2\) zeros at both ends of the protein sequence. As the sliding window slides, calculate the mean value \(V_{i}\) of the five-dimensional feature vector of each window, including Shannon entropy, topological entropy, permutation entropy, and two amino acid preferences, and assign \(V_{i}\) to all residues in the window. Finally, Dividing the accumulated value of all  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **A** & **R** & **N** & **D** & **C** & **Q** & **E** & **G** & **H** & **I** \\ \hline P = 1 & 0.1739 & \(-\)0.0537 & \(-\)0.2141 & 0.2911 & \(-\)0.5301 & 0.3088 & 0.5214 & 0.0149 & 0.1696 & \(-\)02907 \\ P = 2 & \(-\)0.2750 & \(-\)0.1790 & 0.4790 & 0.4625 & \(-\)0.1255 & \(-\)0.0550 & \(-\)0.2745 & 0.6675 & 0.1350 & \(-\)0.5150 \\  & **L** & **K** & **M** & **F** & **P** & **S** & **T** & **W** & **Y** & **V** \\ P = 1 & \(-\)0.3379 & 0.1984 & \(-\)0.1113 & \(-\)0.8434 & \(-\)0.0558 & 0.2627 & \(-\)0.1297 & \(-\)1.3710 & \(-\)0.8040 & \(-\)0.2405 \\ P = 2 & \(-\)0.4385 & \(-\)0.0495 & \(-\)0.4765 & \(-\)0.4970 & 1.1170 & 0.2965 & 0.1450 & \(-\)0.2570 & 0.0825 & \(-\)0.7055 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mapping values of amino acid sequences according to \(p\)-th preference.  
Figure 1: Calculation process of permutation entropy.  
residues by the number of accumulations, the five-dimensional feature vector \(X_{i}\) of each residue can be obtained:  
\[X_{i}=\left\{\begin{array}{l}\frac{1}{j+L_{0}}\sum\limits_{i=1}^{j+L_{0}}V_{i},1\leq j\leq L_{0}\\ \frac{1}{N}\sum\limits_{i=j+L_{0}-L+1}^{j+L_{0}}V_{i},L_{0}<j\leq N-L_{0}\\ \frac{1}{N_{0}-j-L_{0}+1}\sum\limits_{i=j+L_{0}-L+1}^{N_{0}-L+1}V_{i},N-L_{0}< j\leq N\end{array}\right. \tag{14}\]  
Since the 995,189 residues in the DIS2209 data set are ordered, the 222,034 residues are disordered, and the number of positive and negative samples is unbalanced, we have added an oversampling method to increase the sample size and generate according to the law of samples with fewer categories. More samples of this label make the data tend to be balanced and the prediction results are more accurate. Compared with some existing oversampling schemes, we adopted SMOTE oversampling. The specific steps are as follows:  
Step 1: For each sample of the minority class, use Euclidean distance as the standard to calculate the distance from all samples in the minority class sample set to obtain its k nearest neighbors.  
Step 2: Set a sampling ratio according to the sample imbalance ratio to determine the sampling magnification \(N\). For each minority sample \(x\), randomly select several samples from its k nearest neighbors, assuming that the selected nearest neighbor is \(x_{new}\).  
Step 3: For each neighbor selected at random, it is defined as:  
\[x_{new}=x+rand(0,1)\times|\bar{x}-x| \tag{15}\]