A new taxonomy-based protein fold recognition approach based on autocross-covariance transformation

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DeepSF: deep convolutional neural network for mapping protein sequences to folds (2017, background): “to a target protein, which cannot explain the sequence-structure relationship of the protein. Machine learning methods have been developed to directly classify proteins into different fold categories [15, 16, 17]. However, because traditional machine learning methods cannot classify data into a larg”
Enhanced Protein Fold Prediction Method Through a Novel Feature Extraction Technique (2015, methodology): “Since then, several feature extraction methods have been proposed, which include those based on solvent accessibility [5], [6], physicochemical distance transformation [7], autocross-covariance transformation [8], functional domain [2], sequential evolution information [2], [9]–[12], predicted secon”
mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning (2019, methodology): “We also compared our method with a baseline method, which used SVM as the algorithm and ACC as the features.”
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences (2015, background): “Nucleic acid composition (1) Basic kmer (Kmer) (18) Autocorrelation (2) Dinucleotide-based auto covariance (DAC) (16,21) (3) Dinucleotide-based cross covariance (DCC) (16,21)”