Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins.

Evaluating contribution of different feature encoding schemes With regard to post-mRMR and SU attribute selection, of the PP-based features, AAC, molecular weight, residue volume, flexibility, and partition coefficient of predominantly AA11 turned out to be highly significant for classification, followed by the hydrophobicity of the AAs around the central residue.

also in 132,142 other papers