Protein Secondary Structure Prediction Using FFA Optimized ANN

Abstract

The epidermal growth factor (EGF) family of RTKs plays a vital role in regulation of cell proliferation, differentiation, survival and migration. It carries both restricted and redundant function during developed stage of mammalian and maintains tissues at the adult stage. While the regulation carried by these receptors gets decreased, it will lead to many diseases like cancer among humans. Hence, the understanding about the function and regulation of RTK is essential for the development of drugs for human diseases. As the SS of a protein is responsible for the interactions among proteins, it is difficult for the scientists to understand their mutual relationships and functions. Hence the prediction of PSS is considered as a difficult task. Even though PSO based optimization topology exhibits higher accuracy in PSS prediction there may be some drawbacks, when it is subjected to high-dimensional space. Its convergence rate under high-dimensional space is very low. Hence to overcome this drawback, this work utilized a nature inspired firefly algorithm to tune ANN. It will results in improved convergence rate and also consumes less time with high accuracy.