Deep-PCL: A deep learning model for prediction of cancerlectins and non cancerlectins using optimized integrated features

Abstract

Lectins are types of glycoprotein that have a wide variety of different species which play an important part in tumor discrimination due to their meaningful binding resemblance to different types of saccharide (carbohydrate) groups of the protein. Cancerlectins are those lectins that are firmly identified with specific kinds of proteins, which begin cancer cell endurance, development, metastasis, and spread of cancer. Differentiation of a protein based on its functionality remains a difficult job in the post-genomic era. The study of protein-specific function differentiation plays important role in therapeutic cancer studies. Lab-based methods were presented for prediction of cancerlectins. However, these approaches are expensive and time-consuming. Numerous computational sequence-based approaches have been developed to separate cancerlectins from non-cancerlectins. In our proposed study, we have designed a fast deep learning model for the discrimination of cancerlectins from non-cancerlectins on sequence-based feature descriptive techniques. The proposed model discovered intrinsic features by Conjoint Trade (CT), Pseudo Amino Acid Composition (PseAAC), and Position Specific Scoring Matrix (PSSM). The feature vector of these descriptors was concatenated and selected the best features by Random Forest-Sequential Feature Selection (RF-SFS). The model training and prediction were performed with Decision Tree (DT), Random Forest Classifier (RFC), Support Vector Machine (SVM), and Deep Neural Network (DNN).The DNN showed the best performance and secured 89.40% accuracy, 80.84% sensitivity, and 94.62% specificity. These experimental results show the sturdiness of the proposed study and surpassed all the current methodology in the literature. We believe that the proposed strategy will be a helpful instrument in the malignant growth therapeutics research, drug plan, and scholarly examination considers.