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The digital nature of genomic information makes it suitable for the application of signal processing techniques to better analyze and understand the characteristics of DNA, proteins, and their interaction. Prediction of genes, protein structure, and protein function greatly utilize pattern recognition techniques, in which hidden Markov models, neural networks, and support vector machines play a central role. Signal processing offers a variety of methods from pattern recognition and network analysis for the diagnosis and therapy of genetic diseases. In this paper, we focus on protein secondary structure prediction and discuss the problems in single sequence setting.