Data Analysis and Classification for Bioinformatics
March 1, 2010 by BioinformaticsDirectory.com
Product Description
With the explosion of sequence data in public and private databases and the coming explosion of gene expression data in a similar vein, it is becoming increasingly important to understand how to apply well-established data analysis and data classification methods that have been developed in other fields to this field—to try to make sense of the data, to glean biological insights from it, to categorize the data, and to put all of these to good use in industrial applications.
This book introduces the main methods of data analysis and of data classification–as applied to sequence and gene expression analysis–to the biologist and to the computer scientist in this field. It contains material that is presently being taught by the author in the course Data Analysis, Modeling, and Visualization for Bioinformatics at the University of California, Santa Cruz Extension to workers in the biotechnology industry in Silicon Valley.
Order from Amazon Today Data Analysis and Classification for Bioinformatics
An excellent resource describing the mathematical and algorithmic foundations of many current bioinformatics techniques, “Data Analysis and Classification for Bioinformatics” is concise and easy to read. Especially valuable are the extensive references and web-links provided with each chapter. It’s a great standalone introduction to bioinformatics as well as a reference book that belongs on every bioinformaticist’s bookshelf.
Rating: 5 / 5
If you are looking for a clear, brief and to the point source for probabilistic and statistical basis of data analysis for bioinformatics, I would recommend this book. Such background is critical for serious bioinformatics related research.
The beginning of the book has essential introductory topics such as probability theory, distributions , information theory and clustering methods. Multiple sequence alignments, and construction of phylogenetic trees are also part of the book. Then some other topics such as probability models, model fitness, neural networks, decision trees and protein structure prediction are included. I think that literature references as concrete examples and also links to valuable sources are very beneficial.
It is inevitable that a book on this topic will have some mathematical formula and examples of application of such formula. An advantage of this book is that the formula and examples used in the book are predominantly from the area of bioinformatics. One very simple observation: the book has some basic concepts in the beginning, sample space and a related event. Example used about sample space is s={A, C, G, T}, the four bases of DNA.
The book does not only give background information on probabilistic and statistical basis of data analysis, but it also repeatedly and carefully relates these issues to research in bioinformatics. For example, I was once interested in obtaining some information on the algorithms for phylogenetic tree construction, especially on one of the most common tools, UPGMA. When I was reading the related chapter on clustering methods in this book, I found it there. It was clear, brief and to the point, just the information I needed.
Rating: 5 / 5
As a bioinformatics certificate student in Dr. Jagota’s summer 2000 offering of “Data Analysis, Modeling, and Visualization” at UC Santa Cruz, I have read this text cover-to-cover and have listened to lectures based on the book. In a complete yet concise way, the book begins with basic notions of probability theory, distributions, tests of statistical significance, and information theory, with many illustrative examples from molecular biology (DNA/nucleotides and proteins/amino acids). Next, clustering methods are discussed (k-means, hierarchical, self-organizing maps), and current applications to such problems as gene expression data from microarrays, multiple alignments, and phylogenetic tree construction are included. Different types of probability models (independent, markov, mixture) as well as model fitness, training, and overfitting are reviewed, and the author clearly distinguishes the appropriateness of each via simple, yet revealing molecular biology examples. Once the supervised classification problem is defined and supervised probability models are introduced, an in-depth development of neural networks, decision trees, and nearest-neighbor classifiers follows, with algorithms for implementation and applications of each to gene finding and protein structure prediction. Finally, each chapter of the text ends with numerous, valuable URL’s and references that I found particularly useful. I highly recommend this book both for molecular biologists entering the computational arena as well as for applied mathematicians, statisticians, and computer scientists looking for a cutting-edge field of application.
Rating: 5 / 5
This book has been used at UCSC extension, and seems most of the students are using this book like a lecture/summary notes, not as a full textbook. Without attending the class, it is VERY difficult to understand. However, once you attended the class (and study hard with some supplement textbooks if you are a beginner), you will realize that this book summarizes key points very well.
So… if you are a beginner to this field, you might want to look for an alternative. If you are already comfortable with statistics and bioinformatics, and want to check-out key points, maybe you are interested.
Rating: 3 / 5
I bought this book for the Data Analysis, Modeling, and Visualization class run by the author, Arun Jagota. It is little more then the notes he uses in his class bound in a nice little book. There are very few meaningful examples and the text assumes the reader already has a knowledge base in the subject since little detail is given in most areas of the book. If you are going to take this class and have to buy the book then I would suggest saving your money and buying the pdf file version of it and printing it out. Chances are you won’t use this text very often.
Rating: 1 / 5