Overall framework of HLPpred-Fuse. Figure 1. Development and Flowchart of HLPpred-Fuse. It involves the following steps: (i) two separate datasets was constructed for the first- and second-layer prediction model development. (ii) nine feature encodings were utilized and inputted to six different classifiers and developed their corresponding prediction models. Subsequently, the predicted probability of positives was fused and considered as the fused features and inputted to ERT and developed the final prediction model. (iii) construction of two-layer framework. (See reference for more detailed information).
Reference
HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation (Submitted). [Please cite this paper if you find HLPpred-Fuse is useful in your research]
