The pressing need for the prediction of protein function has led to the development of hundreds of different techniques for solving this problem by computational means. For an individual researcher to attempt to directly survey this body of work would be immensely time consuming, and in most cases, impractical. To answer this need, this book provides a comprehensive overview of the field of automated protein function prediction. It covers many techniques for solving this problem by computational means and discusses the most important principles underlying these techniques. The book also describes the types of data used by these methods, including protein sequences, protein structures, complete genomes, phylogenetic profiles, gene expression data, protein interaction networks, biomedical literature, and combinations of these. In addition, it presents several perspectives on important issues in function prediction, such as the advantages of using the Gene Ontology as a functional classification scheme.

This book will be useful for researchers in the area of computational biology or bioinformatics, as well as experimental biologists in universities, research institutes, and commercial organizations, such as companies working on molecular biology projects. The former can learn the basic concepts and techniques underlying the automated prediction of protein function from various types of data, and develop these ideas for their research. After reading this book (or a portion of it), experimental biologists will be better able to identify the most relevant type of data and the most useful prediction technique for generating hypotheses about a protein's f[unction. These hypotheses can then be confirmed by experiments. Thus, this book can serve as a research handbook for practitioners in the field of functional genomics, in particular, and molecular biology, in general.