Predicting Anti-Cancer Peptides using Traditional Classifiers
Cancer imposes a global health burden as it represents one of the leading causes of morbidity and mortality while also giving rise to significant economic burden owing to the associated expenditures for its monitoring and treatment. Anticancer peptides open a promising perspective for cancer treatment, and they have various attractive advantages. As the traditional experiments are expensive and inefficient for predicting ACP, the researchers have proposed A deep learning long short-term memory(LSTM) neural network model, ACP-DL, to effectively predict novel anticancer peptides. In this project, we have used some traditional classifiers to predict anticancer peptides.
This project is supervised by Dr. Khondaker Abdullah Al Mamun Sir (faculty member, Department of CSE, United International University)