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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 |
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Genomics Inform. 2017;15(4):162-169. Published online December 29, 2017 DOI: https://doi.org/10.5808/GI.2017.15.4.162 |
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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 Genomics & Informatics. 2017;15(4):162-169  Predicting Binding Sites of EH1-like Motifs from Their Amino Acid Sequences Biophysical Journal. 2009;96(3):651a-652a Simplified amino acid alphabets based on deviation of conditional probability from random background Physical Review E. 2002;66(2): LabCaS: Labeling calpain substrate cleavage sites from amino acid sequence using conditional random fields Proteins: Structure, Function, and Bioinformatics. 2012;81(4):622-634 Prediction of ��-turns from amino acid sequences using the residue-coupled model The Journal of Peptide Research. 2008;61(4):159-162 AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices Amino Acids. 2007;35(2):345-353 Grouping of amino acid types and extraction of amino acid properties from multiple sequence alignments using variance maximization Proteins: Structure, Function, and Bioinformatics. 2005;61(3):523-534 Accuracy of sequence alignment and fold assessment using reduced amino acid alphabets Proteins: Structure, Function, and Bioinformatics. 2006;63(4):986-995 Identification of three pertussis toxin substrates (41, 40 and 39 kDa proteins) in mammalian brain Comparison of predicted amino acid sequences from G-protein α-subunit genes and cDNAs with partial amino acid sequences from purified proteins FEBS Letters. 1988;230(1-2):85-89 PRINTR: Prediction of RNA binding sites in proteins using SVM and profiles Amino Acids. 2008;35(2):295-302 |
