An efficient approach for the prediction of ion channels and their subfamilies
In this paper, random forest based approach is proposed to predict ion channels families and their subfamilies by using sequence derived features. Here, amino acid composition, dipeptide composition, correlation features, composition, transition and distribution and pseudo amino acid composition are used to represent the protein sequence. The minimum redundancy and maximum relevance feature selection is used to find the optimal number of features for improving the prediction performance. Obtained high accuracies, MCC and ROC area values.
ion channels random forest voltage gated ion channels ligand gated ion channels minimum redundancy maximum relevance sodium potassium calcium chloride