Prediction of Intrinsically Disordered Proteins Using Machine Learning Algorithms Based on Fuzzy Entropy Feature

We used fuzzy entropy as a feature to optimize the intrinsically disordered protein prediction scheme. The optimization scheme requires computing only five features for each residue of a protein sequence, that is, the Shannon entropy, topological entropy, and the weighted average values of two propensities. Notably, this is the first time that fuzzy entropy has been applied to the field of protein sequencing. In addition, we used three machine learning to examine the prediction results before and after optimization. The results show that the use of fuzzy entropy leads to an improvement in the performance of different algorithms, demonstrating the generality of its application. Finally, we compare the simulation results of our scheme with those of some existing schemes to demonstrate its effectiveness.