iHSP-PseRAAAC: Identifying the heat shock protein families using pseudo reduced amino acid alphabet composition

iTIS-PseTNC: A sequence-based predictor for identifying translation initiation site in human genes using pseudo trinucleotide composition

“…Using Eq. (16) would make the meaning of the four metrics crystal clear even for experimental scientists as elaborated in [10,12,69,70].…”

Simplification of complexity in protein molecular systems by grouping amino acids: a view from physics

“…In bioinformatics, the simplified amino acid alphabets are also widely used. The simplified alphabets are used, but not limited, in the prediction of protein-protein interaction and interface [126][127][128], nuclear receptors [129], DNA-binding proteins [130], defensin family and subfamily [131], heat shock protein family [132], residue flexibility [133,134], ability of protein crystallization [135] and various kinds of applications [136,137]. For example, the simplified alphabet (six groups) could be used to detect the sequence conservation related to the super-families of proteins [138,139].…”

Pse-in-One 2.0: An Improved Package of Web Servers for Generating Various Modes of Pseudo Components of DNA, RNA, and Protein Sequences

“…This is because almost all the existing machine-learning algorithms, such as "Neural Network" or NN algorithm [1][2][3] "Support Vector Machine" or SVM algorithm [4][5][6][7][8][9][10][11][12] "Nearest Neighbor" or NN algorithm [13,14] and "Random Forest" algorithm [15][16][17][18][19][20][21][22] can only handle vectors but not sequence samples as elucidated in a review paper [23]. Unfortunately, if using the sequential model, i.e., the model in which all the samples are represented by their original sequences, it is hardly able to train a machine learning model that can cover all the possible cases concerned, as elaborated in [24].…”