title: Elman RNN based classification of proteins sequences on account of their mutual information.
abstract: In the present work we have employed the method of estimating residue correlation within the protein sequences, by using the mutual information (MI) of adjacent residues, based on structural and solvent accessibility properties of amino acids. The long range correlation between nonadjacent residues is improved by constructing a mutual information vector (MIV) for a single protein sequence, like this each protein sequence is associated with its corresponding MIVs. These MIVs are given to Elman RNN to obtain the classification of protein sequences. The modeling power of MIV was shown to be significantly better, giving a new approach towards alignment free classification of protein sequences. We also conclude that sequence structural and solvent accessible property based MIVs are better predictor.
authors: Center of Bioinformatics, University of Allahabad, Allahabad 211002, India. pooja.mishra0806@gmail.com
authors: Pooja
authors: P
authors: Mishra
authors: Paras
authors: P
authors: Nath Pandey
mesh_terms: D030562:Databases, Protein; D008958:Models, Molecular; D011506:Proteins / Q000145:classification* / Q000235:genetics*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins