Classification Analysis Of Dna Microarrays
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Author |
: Leif E. Peterson |
Publisher |
: Wiley-IEEE Computer Society Press |
Total Pages |
: 736 |
Release |
: 2012-12-18 |
ISBN-10 |
: 1118453050 |
ISBN-13 |
: 9781118453056 |
Rating |
: 4/5 (50 Downloads) |
Synopsis Classification Analysis of DNA Microarrays by : Leif E. Peterson
Wide coverage of traditional unsupervised and supervised methods and newer contemporary approaches that help researchers handle the rapid growth of classification methods in DNA microarray studies Proliferating classification methods in DNA microarray studies have resulted in a body of information scattered throughout literature, conference proceedings, and elsewhere. This book unites many of these classification methods in a single volume. In addition to traditional statistical methods, it covers newer machine-learning approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, swarm intelligence involving particle swarm optimization, and more. Classification Analysis of DNA Microarrays provides highly detailed pseudo-code and rich, graphical programming features, plus ready-to-run source code. Along with primary methods that include traditional and contemporary classification, it offers supplementary tools and data preparation routines for standardization and fuzzification; dimensional reduction via crisp and fuzzy c-means, PCA, and non-linear manifold learning; and computational linguistics via text analytics and n-gram analysis, recursive feature extraction during ANN, kernel-based methods, ensemble classifier fusion. This powerful new resource: Provides information on the use of classification analysis for DNA microarrays used for large-scale high-throughput transcriptional studies Serves as a historical repository of general use supervised classification methods as well as newer contemporary methods Brings the reader quickly up to speed on the various classification methods by implementing the programming pseudo-code and source code provided in the book Describes implementation methods that help shorten discovery times Classification Analysis of DNA Microarrays is useful for professionals and graduate students in computer science, bioinformatics, biostatistics, systems biology, and many related fields.
Author |
: Leif E. Peterson |
Publisher |
: John Wiley & Sons |
Total Pages |
: 752 |
Release |
: 2013-06-24 |
ISBN-10 |
: 9780470170816 |
ISBN-13 |
: 0470170816 |
Rating |
: 4/5 (16 Downloads) |
Synopsis Classification Analysis of DNA Microarrays by : Leif E. Peterson
Wiley Series in Bioinformatics: Computational Techniques and Engineering Yi Pan and Albert Y. Zomaya, Series Editors Wide coverage of traditional unsupervised and supervised methods and newer contemporary approaches that help researchers handle the rapid growth of classification methods in DNA microarray studies Proliferating classification methods in DNA microarray studies have resulted in a body of information scattered throughout literature, conference proceedings, and elsewhere. This book unites many of these classification methods in a single volume. In addition to traditional statistical methods, it covers newer machine-learning approaches such as fuzzy methods, artificial neural networks, evolutionary-based genetic algorithms, support vector machines, swarm intelligence involving particle swarm optimization, and more. Classification Analysis of DNA Microarrays provides highly detailed pseudo-code and rich, graphical programming features, plus ready-to-run source code. Along with primary methods that include traditional and contemporary classification, it offers supplementary tools and data preparation routines for standardization and fuzzification; dimensional reduction via crisp and fuzzy c-means, PCA, and non-linear manifold learning; and computational linguistics via text analytics and n-gram analysis, recursive feature extraction during ANN, kernel-based methods, ensemble classifier fusion. This powerful new resource: Provides information on the use of classification analysis for DNA microarrays used for large-scale high-throughput transcriptional studies Serves as a historical repository of general use supervised classification methods as well as newer contemporary methods Brings the reader quickly up to speed on the various classification methods by implementing the programming pseudo-code and source code provided in the book Describes implementation methods that help shorten discovery times Classification Analysis of DNA Microarrays is useful for professionals and graduate students in computer science, bioinformatics, biostatistics, systems biology, and many related fields.
Author |
: Simon M. Lin |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 192 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461508731 |
ISBN-13 |
: 1461508738 |
Rating |
: 4/5 (31 Downloads) |
Synopsis Methods of Microarray Data Analysis by : Simon M. Lin
Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis is one of the first books dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods ranging from data normalization, feature selection and discriminative analysis to machine learning techniques. Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis focuses on two well-known data sets, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.
Author |
: Dhammika Amaratunga |
Publisher |
: John Wiley & Sons |
Total Pages |
: 320 |
Release |
: 2014-01-27 |
ISBN-10 |
: 9781118364529 |
ISBN-13 |
: 111836452X |
Rating |
: 4/5 (29 Downloads) |
Synopsis Exploration and Analysis of DNA Microarray and Other High-Dimensional Data by : Dhammika Amaratunga
Praise for the First Edition “...extremely well written...a comprehensive and up-to-date overview of this important field.” – Journal of Environmental Quality Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition provides comprehensive coverage of recent advancements in microarray data analysis. A cutting-edge guide, the Second Edition demonstrates various methodologies for analyzing data in biomedical research and offers an overview of the modern techniques used in microarray technology to study patterns of gene activity. The new edition answers the need for an efficient outline of all phases of this revolutionary analytical technique, from preprocessing to the analysis stage. Utilizing research and experience from highly-qualified authors in fields of data analysis, Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition features: A new chapter on the interpretation of findings that includes a discussion of signatures and material on gene set analysis, including network analysis New topics of coverage including ABC clustering, biclustering, partial least squares, penalized methods, ensemble methods, and enriched ensemble methods Updated exercises to deepen knowledge of the presented material and provide readers with resources for further study The book is an ideal reference for scientists in biomedical and genomics research fields who analyze DNA microarrays and protein array data, as well as statisticians and bioinformatics practitioners. Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition is also a useful text for graduate-level courses on statistics, computational biology, and bioinformatics.
Author |
: Steen Knudsen |
Publisher |
: John Wiley & Sons |
Total Pages |
: 148 |
Release |
: 2011-09-23 |
ISBN-10 |
: 9780471461180 |
ISBN-13 |
: 0471461180 |
Rating |
: 4/5 (80 Downloads) |
Synopsis A Biologist's Guide to Analysis of DNA Microarray Data by : Steen Knudsen
A great introductory book that details reliable approaches to problems met in standard microarray data analyses. It provides examples of established approaches such as cluster analysis, function prediction, and principle component analysis. Discover real examples to illustrate the key concepts of data analysis. Written for those without any advanced background in math, statistics, or computer sciences, this book is essential for anyone interested in harnessing the immense potential of microarrays in biology and medicine.
Author |
: Terry Speed |
Publisher |
: CRC Press |
Total Pages |
: 237 |
Release |
: 2003-03-26 |
ISBN-10 |
: 9780203011232 |
ISBN-13 |
: 0203011236 |
Rating |
: 4/5 (32 Downloads) |
Synopsis Statistical Analysis of Gene Expression Microarray Data by : Terry Speed
Although less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies
Author |
: Richard M. Simon |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 205 |
Release |
: 2006-05-09 |
ISBN-10 |
: 9780387218663 |
ISBN-13 |
: 0387218661 |
Rating |
: 4/5 (63 Downloads) |
Synopsis Design and Analysis of DNA Microarray Investigations by : Richard M. Simon
The analysis of gene expression profile data from DNA micorarray studies are discussed in this book. It provides a review of available methods and presents it in a manner that is intelligible to biologists. It offers an understanding of the design and analysis of experiments utilizing microarrays to benefit scientists. It includes an Appendix tutorial on the use of BRB-ArrayTools and step by step analyses of several major datasets using this software which is available from the National Cancer Institute.
Author |
: Simon M. Lin |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 214 |
Release |
: 2007-05-08 |
ISBN-10 |
: 9780306475986 |
ISBN-13 |
: 0306475987 |
Rating |
: 4/5 (86 Downloads) |
Synopsis Methods of Microarray Data Analysis II by : Simon M. Lin
Microarray technology is a major experimental tool for functional genomic explorations, and will continue to be a major tool throughout this decade and beyond. The recent explosion of this technology threatens to overwhelm the scientific community with massive quantities of data. Because microarray data analysis is an emerging field, very few analytical models currently exist. Methods of Microarray Data Analysis II is the second book in this pioneering series dedicated to this exciting new field. In a single reference, readers can learn about the most up-to-date methods, ranging from data normalization, feature selection, and discriminative analysis to machine learning techniques. Currently, there are no standard procedures for the design and analysis of microarray experiments. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter. Real examples expose the strengths and weaknesses of each method for a given situation, aimed at helping readers choose appropriate protocols and utilize them for their own data set. In addition, web links are provided to the programs and tools discussed in several chapters. This book is an excellent reference not only for academic and industrial researchers, but also for core bioinformatics/genomics courses in undergraduate and graduate programs.
Author |
: Daniel P. Berrar |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 382 |
Release |
: 2007-05-08 |
ISBN-10 |
: 9780306478154 |
ISBN-13 |
: 0306478153 |
Rating |
: 4/5 (54 Downloads) |
Synopsis A Practical Approach to Microarray Data Analysis by : Daniel P. Berrar
In the past several years, DNA microarray technology has attracted tremendous interest in both the scientific community and in industry. With its ability to simultaneously measure the activity and interactions of thousands of genes, this modern technology promises unprecedented new insights into mechanisms of living systems. Currently, the primary applications of microarrays include gene discovery, disease diagnosis and prognosis, drug discovery (pharmacogenomics), and toxicological research (toxicogenomics). Typical scientific tasks addressed by microarray experiments include the identification of coexpressed genes, discovery of sample or gene groups with similar expression patterns, identification of genes whose expression patterns are highly differentiating with respect to a set of discerned biological entities (e.g., tumor types), and the study of gene activity patterns under various stress conditions (e.g., chemical treatment). More recently, the discovery, modeling, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added to the list of scientific tasks that are being tackled by microarray technology. Each scientific task corresponds to one or more so-called data analysis tasks. Different types of scientific questions require different sets of data analytical techniques. Broadly speaking, there are two classes of elementary data analysis tasks, predictive modeling and pattern-detection. Predictive modeling tasks are concerned with learning a classification or estimation function, whereas pattern-detection methods screen the available data for interesting, previously unknown regularities or relationships.
Author |
: Aidong Zhang |
Publisher |
: World Scientific Publishing Company |
Total Pages |
: 356 |
Release |
: 2006-06-27 |
ISBN-10 |
: 9789813106642 |
ISBN-13 |
: 9813106646 |
Rating |
: 4/5 (42 Downloads) |
Synopsis Advanced Analysis Of Gene Expression Microarray Data by : Aidong Zhang
This book focuses on the development and application of the latest advanced data mining, machine learning, and visualization techniques for the identification of interesting, significant, and novel patterns in gene expression microarray data.Biomedical researchers will find this book invaluable for learning the cutting-edge methods for analyzing gene expression microarray data. Specifically, the coverage includes the following state-of-the-art methods:• Gene-based analysis: the latest novel clustering algorithms to identify co-expressed genes and coherent patterns in gene expression microarray data sets• Sample-based analysis: supervised and unsupervised methods for the reduction of the gene dimensionality to select significant genes. A series of approaches to disease classification and discovery are also described• Pattern-based analysis: methods for ascertaining the relationship between (subsets of) genes and (subsets of) samples. Various novel pattern-based clustering algorithms to find the coherent patterns embedded in the sub-attribute spaces are discussed• Visualization tools: various methods for gene expression data visualization. The visualization process is intended to transform the gene expression data set from high-dimensional space into a more easily understood two- or three-dimensional space.