Neural Network Foundations

Artificial neural networks are versatile tools for a number of applications, including bioinformatics. However, they are not thinking machines nor are they black boxes to blindly feed data into with expectations of miraculous results. Neural networks are typically computer software implementations of algorithms, which fortunately may be represented by highly visual, often simple diagrams. Neural networks represent a powerful set of mathematical tools, usually highly nonlinear in nature, that can be used to perform a number of traditional statistical chores such as classification, pattern recognition and feature extraction. 

The purpose of the following four chapters is to build a foundation of understanding of basic neural network principles. Subsequent chapters will address issues specific to choosing the neural network design (architecture) for particular applications and the preparation of data (data encoding) for use by neural networks. 

Not all neural networks are the same their connections, elemental functions, training methods and applications may differ in significant ways. The types of elements in a network and the connections between them are referred to as the network architecture. Commonly used elements in artificial neural networks will be presented in Chapter 2. The multilayer perception, one of the most commonly used architectures, is described in Chapter 3. Other architectures, such as radial basis function networks and self organizing maps (SOM) or Kohonen architectures, will be described in Chapter 4. 

Coupled closely with each network architecture is its training method. Training (or as it is sometimes called, learning) is a means of adjusting the connections between elements in a neural network in response to input data so that a given task can be performed. A 

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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. 

Hayldn, S. Neural Networks A Comprehensive Foundation, Macmillan, New York (1994). 

S. Haykin, Neural Networks—A Comprehensive Foundation, Macmillan College Publishing Company, NY... 

A helpful starting point for further investigation is Learning Classifier Systems From Foundations to Applications.1 The literature in classifier systems is far thinner than that in genetic algorithms, artificial neural networks, and other methods discussed in this book. A productive way to uncover more... 

S. Haykin, Neural Networks a Comprehensive Foundation, 2nd edn., Prentice Hall, Englewood Cliffs, NJ, 1998. 

Haykin, S.Neural Networks A Comprehensive Foundation, Upper Saddle River, NJ Prentice Hall, 1999. 

Haykin, S. (1994). Neural Networks, A Comprehensive Foundation. MacMillan College Publishing, New York. 

Haykin S. Neural networks A comprehensive foundation. New York Macmillan, 1994. 

Haykin, S. (1999). Neural Networks A Comprehensive foundation, 2nd edn. (Prentice-Hall Internal Inc., New Jersey). 

Haykin, S. Neural Networks A Comprehensive Foundation. New York Macmillan/IEEE Press, 1994. Egmont-Petersen, M., de Ridder, D., and Handels, H. "Image Processing with Neural Networks A Review," Pattern Recognition 35 (2002) 2279-2301. 

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