Neural Networks for Genome Informatics

Artificial neural networks are now widely used to solve various problems in genome informatics and molecular sequence analysis. Part III provides an in-depth discussion of special system designs and considerations for building neural networks for genome informatics applications (chapters 6-8), and broad reviews of state-of-the-art methods and their evaluations (chapters 9-11). 

Figure 6.1 Design issues of neural network applications for genome informatics.

The many features and advantages of the neural network method have made it an important research tool for genome informatics. As a useful adjunct to other statistical and mathematical methods, neural networks will continue to play important roles in life science, where complex biological knowledge cannot be easily modeled, and will help us understand and answer fundamental biological questions. 

Despite the fact that the neural network literature increasingly contains examples of radial basis function network applications, their use in genome informatics has rarely been -reported—not because the potential for applications is not there, but more likely due to a lag time between development of the technology and applications to a given field. Casidio et al. (1995) used a radial basis function network to optimally predict the free energy contributions due to hydrogen bonds, hydrophobic interactions and the unfolded state, with simple input measures. 

How have neural networks been used in genome informatics applications In Part II, we have summarized them based on the types of applications for DNA sequence analysis, protein structure prediction and protein sequence analysis. Indeed, the development of neural network applications over the years has resulted in many successful and widely used systems. Current state-of-the-art systems include those for gene recognition, secondary structure prediction, protein classification, signal peptide recognition, and peptide design, to name just a few. 

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