## 1 Introduction  
As the highest content of organic compounds in human body, protein is the main bearer of human life activities, The \"Amino Acid Sequence--3-Dimensional Structure--Protein Function\" paradigm of protein was generally accepted [1]. However, in the past few decades, it has been found that not all proteins have a fixed three-dimensional structure in the whole sequence, and a protein lacking a specific three-dimensional structure has been continuously discovered by researchers [2]. These proteins lack a stable three-dimensional structure in at least one region and can also perform normal biological functions, so they are called intrinsically disordered proteins (IDPS). IDPS play an important role in physiological processes such as DNA transcription and translation [3]. Studies have shown that disordered proteins are associated with some major human diseases. For example, the lack of IDPS functionality may induce heart disease, Parkinson's disease, nerve tissue disease, cancer, etc. [4; 5; 6; 7; 8]. For example, the first pathogenic mutation in the SNCA gene, encoding for \(\alpha\)-synuclein was discovered in cases of familial Parkinson's disease [9], some of these point mutations cause Parkinson's with high penetrance. In addition, the severity of cognitive impairment in [PERSON]'s disease was later shown to better correlate with low-molecular weight and soluble amyloid-beta aggregates, when amyloid-beta is highly disorganized in shape, it's actually less likely to stick together and form toxic clusters that lead to brain cell death [10]. A considerable number of biophysical studies have shown, type-2 diabetic islets are characterized by islet amyloid protein derived from islet amyloid peptide (IAPP), a protein co-expressed by beta cells with insulin that, when misfoldedand present in aggregated form, may lead to beta cell failure [11]. Therefore, more and more attention has been paid to the study of disordered proteins in recent years, and the research on the characteristics, functions and prediction of disordered proteins has also been greatly developed. In the past few decades, there are various schemes for predicting IDPS that continue to emerge, and these methods are roughly divided into two categories: physicochemical-based and calculation-based. The first method is to detect IDPs by using amino acid propensity scale and physicochemical properties of protein sequence, such as GlobPlot [12], IUPred [13], FoldIndex [14] and IsUnstruct [15]. Compared with the physicochemical-based method, The second method distinguishes ordered and disordered proteins with positive samples and negative samples, effectively combines various features, and uses machine learning to make predictions, such as support vector machines (SVM), Naive Bayes (NB), K nearest neighbors (KNN) and decision trees (DT). These schemes include DISOPRED3 [16], SPINE-D [17], ESpritz [18] and MetaDisorder [19]. DISOPRED3 calculates the Position-Specific Substitution Matrix (PSSM) of all residues using three iterations of PSI-BLAST, and predicts the disordered regions and protein binding sites by using support vector machines as classifiers. SPINE-D uses a neural network to predict disorder regions, the algorithm makes a ternary prediction of all residues (ordered residues, short disordered region residues and long disordered region residues), then simplifies it to a binary prediction and trains both short disordered regions and long disordered regions. It is worth mentioning that the hybrid scheme based on various predictors can integrate a plurality of single schemes, and can better utilize the prediction advantages of different aspects of each single scheme, so as to improve the prediction accuracy. For example, MetaDisorder integrates predictors including DISOPRED2 [20], Globplot, IUpred, PrDOS [21], POODLE [22], DISPI [23], RONN [24], etc., So that its prediction results score higher than a single prediction result.