###### Abstract  
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. intrinsically disordered proteins; fuzzy entropy; machine learning; prediction +
Footnote †: journal: Applied Soft Matter  
gorithm to distinguish amino acids by the different components of ordered and disordered amino acids. POODLE series of algorithms include POODLE-S [11], POODLE-L [12] and POODLEW [13] algorithms. They predicted regions with lengths less than 40, greater than 40, and overall disorder. Because of some differences in the structure of their prediction targets, their feature selection is slightly different. In addition, ESpritz [14] algorithm is also a relatively successful algorithm. Espritz can reduce the number of parameters and extract the hidden information from the sequence by recursive dynamic learning context. In addition to the conventional machine learning algorithm, the comprehensive prediction method based on a variety of independent prediction algorithms also shows good results in the prediction of intrinsically disordered proteins. The method is to run several independent intrinsically disordered protein prediction algorithms, and then get the final result through comprehensive prediction. Metadisorder is a very accurate algorithm. It includes 13 kinds of algorithms: Globplot, IUPred, DisEMBL, RONN, POODLES, DISPRED2 [15], DISPI [16], IPDA [17], PDISORDER [18], POODLE-L, PRDOS [19], Spritz [20], dispsmp [21]. The final results are weighted by these 13 algorithms, which have high accuracy. However, the efficiency of the algorithm is very low because it contains 13 independent prediction algorithms. In comparison, MFDP [22] with only three independent prediction algorithms is a relatively simple model. It integrates the results of IUPred, DISOPRED2, DISOClust [23] algorithms into SVM with linear kernel to get the final prediction. Most current machine learning methods for intrinsically disordered protein prediction require as many as tens of features and have high computational complexity; they are less efficient when dealing with large amounts of data. We propose a new feature scheme based on fuzzy entropy; and test the feasibility of the scheme using three machine learning algorithms. Our scheme is trained and tested by the dataset DIS1616 with 10-fold cross-validation, firstly. The dataset DIS1616 is comprised of 1616 protein sequences with 2503 disordered regions and 2629 ordered regions, which include 186,069 disordered and 715,619 ordered residues. As a comparison, we run our schemes together with some existing schemes, such as DISOPRED2, RONN [24], DISPSMP [25], Espritz and IsUnstructure [9] on the datasets R80 which are comprised of 78 protein sequences which include 2439 disordered and 19,412 ordered residues. The simulation results suggest that our scheme is at least as accurate as DiISPSSMP and requires computing only five features for each residue of a protein sequence, while the other need to compute 120 features for each residue, respectively.