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Prediction of Metal Ion Binding Sites in Proteins from Amino Acid Sequences by Using Simplified Amino Acid Alphabets and Random Forest Model |
Suresh Kumar |
Genomics Inform. 2017;15(4):162-169. Published online December 29, 2017 DOI: https://doi.org/10.5808/GI.2017.15.4.162 |
Prediction of Metal Ion Binding Sites in Proteins from Amino Acid Sequences by Using Simplified Amino Acid Alphabets and Random Forest Model Simplified amino acid alphabets based on deviation of conditional probability from random background Prediction of presence and absence of two- and three-amino-acid sequences of human monoamine oxidase from its amino acid composition according to the random mechanism Prediction of ��-turns from amino acid sequences using the residue-coupled model Predicting Binding Sites of EH1-like Motifs from Their Amino Acid Sequences Characterization and Prediction of Dengue Virus Targeting Peptides Based on Combined Amino Acid Composition Descriptors Using Random Forest Algorithm. The evolution of proteins from random amino acid sequences. I. Evidence from the lengthwise distribution of amino acids in modern protein sequences Prediction and analysis of cell-penetrating peptides using pseudo-amino acid composition and random forest models Protein Tertiary Structures: Prediction from Amino Acid Sequences Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimensional structures of proteins and its implications for secondary structure prediction from amino acid sequences |