iPredCNC: Computational prediction model for cancerlectins and non-cancerlectins using novel cascade features subset selection

Abstract Lectins are special types of protein that play a crucial role in tumor cell differentiation due to their significant binding affinity to certain types of saccharide (carbohydrate) groups. They are also closely related to certain types of proteins that initiate tumor cell survival, growth, metastasis, carcinoma, and different stages of tumor. Differentiating the specific functions of proteins remains challenging in the post-genomic era. This endeavor is vital in therapeutic cancer studies, but web-lab experiments related to this issue are expensive and time-consuming. To cope with this situation, several computational sequence-based methods have been proposed to differentiate the specific functions of proteins. In the current study, we have developed a fast-accurate cascade feature selection-based machine learning model for cancer lectins using different sequence-based feature descriptive techniques. This model yielded 85.21% accuracy, 87.84% sensitivity, 81.92% specificity, and 0.922 AUC with a multilayer perceptron over k-fold, and stratified k-fold cross-validation tests. These concrete empirical results show the authenticity and robustness of the proposed study compared to all existing approaches. This proposed novel methodology would be a handy tool in cancer therapeutics research, drug design, and academic studies. All the source codes and data regarding this manuscript are freely available via http://www.github.com/zaheeerkhancs/iPredCNC .