Employing a Novel Tri-code Embedding vector with LSTM and SoftMax layer for Membrane Protein Classification

Abstract

Abstract Membrane proteins provide a significant part in cellular activities. The role of membrane proteins is inevitable in drug interactions and in all living organisms. Membrane protein classification is used to identify the relationships between proteins. With the help of amino acid composition, proteins get classified. A novel protein classification scheme is proposed using Tri-code Embedding vector. The results are investigated applying the self-consistency test, the Mathew’s correlation coefficient and the independent data set. Moreover, the proposed method shows its improvement in protein classification process in terms of accuracy, specificity and sensitivity. Thus, the proposed scheme provides an effective protein classification scheme that incorporates the optimistic features of deep learning.