## 3 Feature Encoding Methods
### Disorder-Based Methods  
Disorder  
In the context of protein sequences, disorder refers to intrinsic disordered regions (IDRs) or intrinsically disordered proteins (IDPs) that lack a fixed or ordered three-dimensional structure under physiological conditions [104; 105; 106]. These regions or proteins are highly flexible and dynamic, which allows them to adopt multiple conformations and participate in various biological processes. Disordered regions are highly flexible and can adopt multiple conformations. This flexibility allows them to interact with a wide range of binding partners, often in a context-dependent manner. Despite their lack of a fixed structure, disordered regions play crucial roles in various biological processes, including signal transduction, molecular recognition, and protein-protein interactions. Disordered regions often contain a high proportion of hydrophilic and charged amino acids, which contributes to their flexibility and dynamic nature. They are typically enriched with amino acids such as serine (S), threonine (T), glutamine (Q), and proline (P). Identifying disordered regions in protein sequences is important for understanding their functional roles and for predicting their interactions with other biomolecules. Disorder prediction tools are commonly used in bioinformatics to identify these regions. **DisorderC**  
In the context of protein sequences, disorder C refers to a specific type of intrinsic disorder prediction that focuses on the compositional bias of amino acids within a protein sequence [107]. This method is part of a broader set of approaches used to identify intrinsically disordered regions (IDRs) [108] or intrinsically disordered proteins (IDPs) [109] on the basis of the over-representation or under-representation of certain amino acids. Disorder C identifies regions of a protein sequence that have a significant compositional bias towards certain amino acids. This bias is often associated with intrinsic disorder because some amino acids are more likely to be found in disordered regions. Amino acids such as alanine (A), glycine (G), proline (P), serine (S), and threonine (T) are often over-represented in disordered regions. Conversely, amino acids such as tryptophan (W), tyrosine (Y), and valine (V) are typically under-represented. Disorder C uses statistical methods to compare the observed frequency of each amino acid in a given sequence segment to the expected frequency on the basis of a reference dataset. Segments with significant deviations from the expected frequencies are flagged as potentially disordered. Disorder C can be used in conjunction with other disorder prediction methods to improve the accuracy of identifying disordered regions. These methods may include sequence complexity analysis, secondary structure prediction, and evolutionary conservation. **DisorderB**  
Disorder binary (DisorderB) is a method used in bioinformatics to predict intrinsically disordered regions (IDRs) within protein sequences by classifying each amino acid position in the sequence as either \"ordered\" or \"disordered\" [110]. This binary classification approach simplifies the prediction of disorders and can be particularly useful for high-throughput analysis and preliminary screening of protein sequences. Each amino acid position in the protein sequence is classified into one of the following two categories: ordered (0) or disordered (1). This binary classification provides a straightforward and computationally efficient way to identify disordered regions. Disorder binary predictions are typically generated via machine learning models trained on known examples of disordered and ordered regions. These models can be based on various features, such as amino acid composition, sequence complexity, and physicochemical properties. The binary classification approach is well suited for high-throughput analysis, allowing for the rapid screening of large datasets to identify potential disordered regions in protein sequences.