Bioinformatics: The Machine Learning Approach

"Bioinformatics", by Baldi and Brunak, is a very well-written treatment of current stochastic algorithmics of genomics and proteomics. It is profitable reading for both the computer scientist learning relevant biology and the computational biologist learning relevant computer science. It probably favours the biologist slightly in this regard, as witnessed by my own enthusiasm for this work. Of particular value are the chapters on hidden markov processes and stochastic grammars. The treatment builds smoothly from early chapters on Bayesian fundamentals in chapter 2, to markov chain monte carlo processes in chapter 3, followed by theory and applications of neural networks, three chapters on hidden markov processes (a fascinating and vital field in modern genomics) and lastly an introductory chapter to the equally important area of stochastic grammars. Other appreciated features include: an up-to-date 452-reference bibliography; a comprehensive survey of web-based resources re both genomic databases and available search engines for DNA, RNA and protein sequence-patterns; in the appendices, there are concise definitional reviews re the coupling of information theory with entropy and aspects of HMM's.Lastly, the price is right, as is most often the case with books from MIT Press.The above authors have succeeded well in illuminating a large piece of a very large (and growing) object: the landscape of modern informational biology. They of course cannot cover it all. Another recent book (1997) that complements this book's particular focus is that of Setubal and Meidanis ("Introduction to Computational Molecular Biology"). These authors offer a greater emphasis on string and graph theoretic approaches to sequencing algorithms and deal more directly with various heuristic approaches to fragment assembly and hybridization mapping.

The book provides an abundance of excellent information of machine learning techniques as applied to biology. I found the presentation of the material to be clear, detailed, with a wealth of support data regarding many of the complex issues of BI. Thanks to Baldi and Brunak, the ideas such as hidden Markov models and applications in molecular biology are dramatically clear.

I found the book very readable, and full of information combining the machine learning approach (neural nets and Hidden Markov models) with biological problems. The wealth of specific biological information bridges the background gap for computer scientists and mathematicians, and the organization of topics is excellent.In the mathematics and computer science community, Baldi is an internationally recognized expert in the fields of neural nets and Hidden Markov models and their applications (for instance, he holds a patent for neural net recognition of fingerprints). Concerning HMM's Baldi and co-workers have found statistical models for protein families, sequence signals for nucleosome centers, etc. Moreover, Baldi, together with Chauvin, has developed a gradient descent parameter update method for HMM's which has no zero probability absorptions, and allows on-line updates, useful features not supported by the standard EM method.From these and other applications, I found the text very useful.

The book of P.Baldi and S.Brunak presents a clear and exhaustive review of the main topics concerning Machine Learning techniques, as well as a broad discussion on the most significant problems that have faced Bioinformatics in recent years together with many hints on the future directions for the ML approach in BI. In the book the description of ML tools (Probabilistic Models, ANNs, HMMs, Hybrid Systems, etc.) unified under the Bayesian framework, is always clear and rigorous. Most of the theoretical materials that are unnecessary for an immediate comprehension -but that some readers may require for a deeper foundation of the ML approach- are presented in the rich appendices, a fair choice to keep the text clear. In any case the specific techniques are described in enough detail, so that any smart reader should be able to implement the models presented without further information. The biological aspects are described at a similar level of detail. As a result the book is very useful both for CS researchers interested in Computational Biology and for Biologists who want to acquire a deeper knowledge of the ML algorithmic tools used for biological data processing. It is obvious that ML plays a broad role in Bioinformatics and that sometimes some of its different aspects seem to be so weakly related that it seems a hard task to systematically review the state of the art of this approach. Anyway, the book of P.Baldi and S.Brunak performs the task successfully and actually represents both the first comprehensive book on ML in Bioinformatics and an incredibly rich pointer to all the resources (books, papers, servers and biological databases on the web) concerning this very promising discipline.

This book is an excellent source of information for beginning the study of machine learning algorithms applied to biology. Reading the book you get a clear feeling that bioinformatics is not just one of the many application fields of computer science and artificial intelligence, it is perhaps the most challenging set of problems for intelligent algorithms not primarily focused on replicating human intelligence. There is an amazing wealth of open problems, some of which apparently very difficult. No doubt that unless you are already an expert you need an accurate map of this complex territory and the book by Baldi and Brunak is an excellent and up-to-date map that may suggest new exciting ideas for research.As a computer scientist I can say that the book is sometimes difficult to read if you have no previous knowledge of biology. This is because the authors didn't take the simplificative approach of reducing biological problems to abstract mathematics. Rather, they preserved the full biological flavor of the problems. Although this approach costs you more at the beginning, you can eventually get a more accurate and nontrivial picture of the problems. My conclusion: it is perhaps unlikely that you can learn about bioinformatics using only this book. However, if you want to learn about bioinformatics, this book is a must-have reference.