Computational Methods in Molecular Biology

Rating:
Summary: Brief glimpses of many topics
Review: I think the book is more about bioinformatics than molecular biology, but good anyway. If a student has already made some headway in bioinformatics, she is likely to want a peek at the different directions and depths that future study will offer. This book gives a good preview.

Its fifteen chapters are almost all by different authors, on different topics, in different styles and with different mathematical tools. Except for some introductory material, each chapter stands by itself. The chapters are uniformly well written, they keep their computations close to the biology, and they offer depth without falling into a morass of detail. The book uses math but is not too demanding; readers should not be put off by its "Computational" title.

Once inside the book, its authors take us on a quick tour of selected topics, including RNA splicing, high-level annotation of DNA meaning, and several views of protein structure. Computational techniques include traditional decision trees, as well as more modern hidden Markov model techniques, neural nets, analogy to vison processing, and much more.

This book presupposes a bit of knowledge of biology, chemistry, and probability, but not daunting amounts. It's a nice way for the beginner to see which directions look most personally interesting. It may also give the more focussed student a quick look at current highlights in nearby fields. This isn't as complete as a 'survey' book would be, but very good in its own way.

(I've had the pleasure of studying under Prof. Kasif, one of the editors and authors, and hope that familiarity has not prejudiced this review.)

Rating:
Summary: covers lots of practical topics, however rather superficial.
Review: This book is introductory. It deals with many topics such as biological sequence analysis, hidden markov model(HMM), gene prediction using Neural Networks, RNA splicing signal model, evolutionary approach and protein structure modeling. It is helpful to glimpse a broad overview of these topics, however the explanations are rather superficial. Especially the chapters covering sequence analysis are too concise.

In this respect, I recommend the following two books for the readers who want more clear and indepth explanations on the sequence analysis.

<Introduction to Computational Molecular Biology> by Joao Carlos Setubal (Contributor), Joao Meidanis, Joao C. Setabal

<Biological Sequence Analysis : Probabilistic Models of Proteins and Nucleic Acids> by Richard Durbin (Editor), S. Eddy, A. Krogh, G. Mitchison (Contributor)

Also for the readers who are interested in bioinformatics tools, I would recommend

<Bioinformatics : A Practical Guide to the Analysis of Genes and Proteins> by Andreas Baxevanis (Editor), B.F.Francis Ouellette (Editor)

Anyway this book covers lots of practical topics and is worth reading.

Rating:
Summary: covers lots of practical topics, however rather superficial.
Review: This book is introductory. It deals with many topics such as biological sequence analysis, hidden markov model(HMM), gene prediction using Neural Networks, RNA splicing signal model, evolutionary approach and protein structure modeling. It is helpful to glimpse a broad overview of these topics, however the explanations are rather superficial. Especially the chapters covering sequence analysis are too concise.

In this respect, I recommend the following two books for the readers who want more clear and indepth explanations on the sequence analysis.

by Joao Carlos Setubal (Contributor), Joao Meidanis, Joao C. Setabal

by Richard Durbin (Editor), S. Eddy, A. Krogh, G. Mitchison (Contributor)

Also for the readers who are interested in bioinformatics tools, I would recommend

by Andreas Baxevanis (Editor), B.F.Francis Ouellette (Editor)

Anyway this book covers lots of practical topics and is worth reading.

Rating:
Summary: A terrific overview of the grand challenges
Review: This book offers a wonderful overview of the computational problems in bioinformatics. Readers with an interest in Artificial Intelligence / Machine Learning will find this book especially worthwhile. There are chapters on hidden Markov models, case-based reasoning, neural networks, evolutionary approaches (Genetic Algorithms), decision-trees, and probabilistic networks, among others. The book covers sequence analysis, gene prediction and annotation, and includes extensive material on protein structure prediction. A must read for anyone interested in Bioinformatics research.

In the interest of full disclosure, I want to mention that one of the editors of this book, Dr. Steven Salzberg, was my graduate advisor when I was a student at Johns Hopkins from 1992 to 1994.

Rating:
Summary: A terrific overview of the grand challenges
Review: This book offers a wonderful overview of the computational problems in bioinformatics. Readers with an interest in Artificial Intelligence / Machine Learning will find this book especially worthwhile. There are chapters on hidden Markov models, case-based reasoning, neural networks, evolutionary approaches (Genetic Algorithms), decision-trees, and probabilistic networks, among others. The book covers sequence analysis, gene prediction and annotation, and includes extensive material on protein structure prediction. A must read for anyone interested in Bioinformatics research.

In the interest of full disclosure, I want to mention that one of the editors of this book, Dr. Steven Salzberg, was my graduate advisor when I was a student at Johns Hopkins from 1992 to 1994.