Neural networks books

Numerous books have been written on the topic of artificial neural networks most are written for, or from the point of view of, computer scientists and these are probably less suited to the needs of experimental scientists than those written with a more general audience in mind. 

Two of the most accessible books in the area are Neural Computing An Introduction, by Beale and Jackson10 and Neural Networks for Chemists, by Zupan and Gasteiger.11 Neither book is a recent publication, but both provide an introduction that is set at a suitable level if you have had little previous contact with ANNs. 

That the SOM is often called a Kohonen map indicates the degree to which Kohonen and his co-workers have helped to define the field. Papers by Kohonen provide a rapid route into work with SOMs, but Zupan and Gasteiger s book Neural Networks for Chemists An Introduction 1 2 3 6 offers a broader look at the techniques and should be helpful for anyone starting work in this area. 

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... 

Models of the form y =f(x) or v =/(x1, x2,..., xm) can be linear or nonlinear they can be formulated as a relatively simple equation or can be implemented as a less evident algorithmic structure, for instance in artificial neural networks (ANN), tree-based methods (CART), local estimations of y by radial basis functions (RBF), k-NN like methods, or splines. This book focuses on linear models of the form... 

Biochemical systems have the advantage of using the language of the physiological processes the biochemical reactions. As such, they can be organized into the neural network-type assemblies in much the way that natural biosystems are. This book is concerned with well-delineated biochemical assemblies and is directed at assessing their ability to perform information-processing operations. In particular, this book is intended ... 

Buhmann, J. Data clustering and learning. In Handbook of Brain Theory and Neural Networks, Arbib, M. (Ed.). Bradfort Books/MIT Press, Cambridge, 1995. 

There have been many books and reviews written on the subject of NN and parallel computing. Only a token one is listed here, for those who need a traditional book reference (Haykin, 1999). It will probably be obsolete before this book is published. Otherwise, a wealth of up-to-date information is always available on the Internet where a neural networks entry produces an avalanche of information. Both lead articles cited for Chapter 10 (Hierlemann et al 1996) and (Jurs et al., 2000) discuss their applications in the context of chemical and biological sensing. 

For a detailed treatment of artificial neural networks, readers are again referred to specific monographs [35, 49-51], for a survey of their applications in chemistry to overview books [52, 53], reviews [54—56], and relevant sections of publications [57-59]. For heterogeneous catalysis, a recent overview has explained the applicability of feedforward networks to the approximation of unknown dependencies and to the extraction of logical rules from experimental data [22]. 

Table 6 contains books on artificial intelligence and neural networks as related to chemistry. Also included are books on chemometrics. Table 7 is a compilation of computational chemistry books focused on pertinent areas of physical chemistry crystallography, spectroscopy, and thermodynamics. 

There are many excellent introductory books and journal articles on the subject of neural networks. Just a few of them are listed below in the references. Additionally, there are tutorials online at various web sites. However, the applications of neural network techniques to problems in molecular biology and genome informatics are largely to be found in scientific journals and symposium proceedings. 

The field of artificial neural networks is a new and rapidly growing field and, as such, is susceptible to problems with naming conventions. In this book, a perceptron is defined as a two-layer network of simple artificial neurons of the type described in Chapter 2. The term perceptron is sometimes used in the literature to refer to the artificial neurons themselves. Perceptrons have been around for decades (McCulloch Pitts, 1943) and were the basis of much theoretical and practical work, especially in the 1960s. Rosenblatt coined the term perceptron (Rosenblatt, 1958). Unfortunately little work was done with perceptrons for quite some time after it was realized that they could be used for only a restricted range of linearly separable problems (Minsky Papert, 1969). 

For a discussion of statistical methods and neural networks, see the book by Ripley (1996) and articles by Ripley (1993), Cheng (Cheng Titterington, 1994) and Sarle (1994). Warren Sarle maintains an excellent neural network FAQ (frequently asked questions) web page for the Usenet newsgroup comp.ai. neural-nets this web page contains many tutorial discussions and references to books, reviews, journal articles and other neural network resources. 

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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