Evaluation of neural network performance in the protein secondary structure prediction problem

In the context of the protein secondary structure prediction problem, we examine the performance of various neural network architectures commonly applied in the literature. Specifically,by applying the neural network paradigm, mappings between training vectors and their desired targets are constructed. The class membership of test data and associated measures of significance are then numerically demonstrated to vary depending on the set of applied target vectors. By applying standard symbol encoding techniques, we analyze and discuss the ability of the neural network to accurately model fundamental attributes of protein secondary structure.