Neural Networks in Bioinformatics

Over the last two decades, neural networks (NNs) gradually became one of the indispensable tools in bioinformatics. This was fueled by the development and rapid growth of numerous biological databases that store data concerning DNA and RNA sequences, protein sequences and structures, and other macromolecular structures. The size and complexity of these data require the use of advanced computational tools. Computational analysis of these databases aims at exposing hidden information that provides insights which help with understanding the underlying biological principles. The most commonly explored capability of neural networks that is exploited in the context of bioinformatics is prediction. This is due to the existence of a large body of raw data and the availability of a limited amount of data that are annotated and can be used to derive the prediction model. In this chapter we discuss and summarize applications of neural networks in bioinformatics, with a particular focus on applications in protein bioinformatics. We summarize the most often used neural network architectures, and discuss several specific applications including prediction of protein secondary structure, solvent accessibility, and binding residues.

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