Genome Informatics Applications

Integration of Statistical Methods into Neural Network Applications.145 

1 Rule and Feature Extraction from Neural Networks.152 

2 Neural Network Design Using Prior Knowledge.156 

Konopka, A. K. (1994). Sequences and codes Fundamentals of biomolecular cryptology. In Biocomputing Informatics and Genome Projects. (Ed., Smith, D.), Academic Press, San Diego, CA. pp. 119- 74. 

Haussler, D. (1994). A hidden Markov model that finds genes in E. coli DNA. Nucleic Acids Res 22,4768-78. 

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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). 

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. 

This part of the book consists of one chapter (Chapter 1) to provide an overview of the domain field, genome informatics, with its major research areas and technologies a brief summary of the computational technology, artificial neural networks , and a summary of genome informatics applications. The latter two topics are further expanded into Part II, Neural Network Foundations, and Part III, Genome Informatics Applications. 

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