Showing posts with label bioinformatics. Show all posts
Showing posts with label bioinformatics. Show all posts

Cancer Mortality and Morbidity Patterns in the U.S. Population: An Interdisciplinary Approach (Statistics for Biology and Health) Review

Cancer Mortality and Morbidity Patterns in the U.S. Population: An Interdisciplinary Approach (Statistics for Biology and Health)
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The authors of this text are a demographer a mathematical physicist and an internal diseases MD. none are professional statisticians but they all have a good understand of mathematics and survival analysis and more importantly each has knowledge about cancer from different perspectives. The theme of the book is that conquering cancer requires an interdisciplinary approach because cancers are complicated diseases and the understanding requires stochastic models and real data. Data on cancer come from many sources. There is the laboratory experiments on cells and animals (often mice), the genetic aspects, the epidemiologic viewpoint and more. The authors know that breakthroughs are occurring on all levels but what has held things back in the compartmentalization of study disciplines and their unique jargon. This creates poor communication and makes it difficult to share results and synthesize results. But a multidisciplinary approach where everyone sheds their jargon and works together to understand what the other person is doing is the efficient way top attain success. I believe this has been proven over and over again in times of war when efficiency becomes a necessity. The Manhattan project with the scientists from various disciplines coming together at Los Alamos under the leadership of J.Robert Oppenheimer is the reason we developed the bomb ahead of Germany and Russia and in time to end the war with Japan.
This book is a compendium of hitory and methods in the fight against cancer and it provides in one source the detailed research from multiple disciplines To model and understand the various types of cancers and their similarities and differences. This is particularly exemplified in chapter 7. Each chapter has an extensive list of references. As the publisher states this book is the first of its kind to describe the interdisciplinary approach in biomedical studies. I agree with that and hope that there will be more to come like this.


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

The Statistics of Gene Mapping (Statistics for Biology and Health)
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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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DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling Review

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