Systems biology, a new and promising field of science that has many applications in different disciplines, is the topic of this book. It deals particularly with information and knowledge in an intelligent way. The book contains a collection of highly interesting research papers, showing the existing methods and tools in the field, along with their merits and limitations. The authors discuss how such techniques can be used and applied correctly and efficiently. In addition, they propose new techniques to deal with information. The book concentrates mainly on biochemistry, biology, and neuroscience phenomena that have applications in machine learning, classification, data mining, modeling, natural language processing, information retrieval, and optimization.

The book is divided into 11 chapters with varying levels of simplicity and complexity. Chapter 1 reviews some machine learning approaches. The authors offer a new approach to structure activity relationships that deals intelligently with data and information representation. They compare their methodology with the existing methods in the field. Chapter 2 discusses neural network-based methods, and shows how they provide a basis for more efficient structure activity prediction models than the well-known linear techniques. Chapter 3 addresses the application of hidden Markov models (that is, Markov chains) to the detection of temporal patterns in the given data. Chapter 4 reviews the optimization genetic algorithms. It discusses inheritance, variation, and selection principles, along with their applications and limitations. Chapter 5 discusses the curses of dimensionality and data sparsity. The authors describe a classification framework that seems to be able to provide robust classification solutions for complex classification problems. Chapter 6 discusses the optimization techniques known as metaheuristic techniques. The authors show their advantages, and discuss future applications. Chapter 7 discusses the integration of multiple data sources to support hypothesis generation and validation using grid computing technologies. Chapter 8 is about the bioinformatics field and its applications, for example, in database querying systems. Chapter 9 addresses the techniques and applications of natural language processing. Chapter 10 discusses the application of neural networks and statistical learning approaches to the modeling of genetic networks. The authors describe new concepts related to clustering and modeling. Chapter 11 presents a computational technique for studying brain function.

As you can see, the book discusses many different aspects of artificial intelligence techniques, as they relate to systems biology. I think this book is fundamental reading for all researchers in information technology who want to learn about the current research in, and future prospects of, artificial intelligence methods and tools. The book is accessible to readers of different backgrounds and levels. I advise students, teachers, researchers, and professionals to read it.