The Statistics of Gene Mapping (Statistics for Biology and Health) Review

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(More customer reviews)David Siegmund is a famous probabilist who is both a great lecturer and writer. I personally audited his advanced probability course at Stanford. He coauthored a book on optimal stopping with Herb Robbins and has written other fine books on sequential analysis and repeated significance testing. In recent years he as well as Brad Efron and other Stanford and Berkeley statistics professors has studied the mathematics, probability theory and statistics associated with human genetics and microarray data. This book presents the theory and application of the appropriate probabilistic methods. Anyone with a serious interest in this topic should get the book.
The book assumes some knowledge of probability and statistics. So a novice in the field of statistics could have trouble with the text and require more development. Also for the statistician it may assume a little too much knowledge of genetics. But I think it is the perfect book for the intended audience and makes a great reference.
Another text that is rigorous in terms of statistics and assume less knowledge of statistics and genetics is "Analyzing Microarray Gene Expression Data" by G. J. McLachlan, k.-A. Do and C. Amboise. You will find that I have also reviewed that text on amazon.

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This book details the statistical concepts used in gene mapping, first in the experimental context of crosses of inbred lines and then in outbred populations, primarily humans. It presents elementary principles of probability and statistics, which are implemented by computational tools based on the R programming language to simulate genetic experiments and evaluate statistical analyses. Each chapter contains exercises, both theoretical and computational, some routine and others that are more challenging. The R programming language is developed in the text.

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Computational Modeling of Genetic and Biochemical Networks (Computational Molecular Biology) Review

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(More customer reviews)Regulatory networks are central to every aspect of computational biology. Determining what they are, and what genes, proteins, and post-translational modifications interact is a major and exciting field of study.
I just didn't come away from this book with that excitement. I was hoping for more about the large-scale regulation networks, but these papers go down to the quantum mechanics of interactions between pairs of molecules. I appreciate that the exact interactions matter, and that computation is probably the only way to examine some kinds of interactions (e.g. the ones in lethal mutations). It's just not what I think of as a "network."
I was also hoping for some more specifics about the computation techniques. There were some interesting insights here. For example, I never thought about the similarities between steady state chemical equilibrium and steady state Markov model behavior before, but the formalisms have striking similarities. I was also interested in some of the information-based measures for determining how well a model represents a system. I learned that the statistical assumptions behind normal chemical "equilibrium" break down at the scale of bacteria - instead, presence or absence of individual molecules matters more. Still, those were isolated kinds of facts and never came together into a whole for me.
The range of views was worthwhile. On the whole, though, the models all seemed very low-level to me, probably not well suited to handling more than a few dozen interactions, and the computation specifics were not always explicit. I'm still looking for a book with more information that I can apply directly.

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The advent of ever more sophisticated molecular manipulation techniques has made it clear that cellular systems are far more complex and dynamic than previously thought. At the same time, experimental techniques are providing an almost overwhelming amount of new data. It is increasingly apparent that linking molecular and cellular structure to function will require the use of new computational tools.This book provides specific examples, across a wide range of molecular and cellular systems, of how modeling techniques can be used to explore functionally relevant molecular and cellular relationships. The modeling techniques covered are applicable to cell, developmental, structural, and mathematical biology; genetics; and computational neuroscience. The book, intended as a primer for both theoretical and experimental biologists, is organized in two parts: models of gene activity and models of interactions among gene products. Modeling examples are provided at several scales for each subject. Each chapter includes an overview of the biological system in question and extensive references to important work in the area.

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DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling Review

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(More customer reviews)This book tries to combine a practical and theoretical point of view concering microarray expermiments and the data analyis thereof. This is a very honourable goal. Unfortunatelly, it fails. An indicator for this can already be seen in the low number of pages. This book has less than 140 pages (I exclude the last chapter and the appendix). It is clear, that it is impossible to discuss in detail this topic in this limited number of pages. Hence, during reading the chapters one gets the feeling, that one reads short essays which are stringed together. At no point the authors go into detail but give only a short idea and references.
I see no reason, why I should recommend this book to anyone. It is in its current form just immature. My prediction: There will be no second edition because even its basic substance is very weak.
Some words to the last chapter (systems biology). This is indeed the most interesting and best chapter of the book (35 pages) without going into details as the rest of the book. I think according to this chapter one realize under which premise this book was written. Unfortunatelly, combining buzz worlds in short essays is not enough for a good book. Sorry guys, I think you can do better!

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Massive data acquisition technologies--such as genome sequencing, high-throughput drug screening, and DNA arrays--are in the process of revolutionizing biology and medicine. This concise, user-friendly and interdisciplinary guide to DNA microarray technology is an introduction and a reference for both biologists and computational scientists. The authors describe the underlying technologies and offer an awareness of the "noise" and pitfalls present in the data generated. They also provide an idea of the different data mining techniques and algorithms that are available to interpret data, and the advantages and disadvantages of each in differing situations.

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