Table 4 LCSD algorithm based on PPRA optimization
Input: Datasets Output: Optimized Hot Region \(LCSD\_Best\_CH_i\) | |
|---|---|
Step 1: Feature selection for residues in datasets; | |
Step 2: A clustering-based boundary point recognition algorithm is used to obtain the predicted set of hot regions LCSD_RH1; | |
Step 3: \(LCSD\_RH1\) is optimized by PPRA to obtain hot region \(LCSD\_RH2\); | |
Step 4: \(LCSD\_RH2\) is optimized according to the loss residue optimization strategy; | |
Step 5: Repeat step 4 until there is no missing residue need to be processed; | |
Step 6: Input the optimized hot regions \(LCSD\_Best\_CH_i\). |