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Analyzing High Dimensional Microarray Data Using Variational Som Microform
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Author :Zhang, Linghai Publisher :Library and Archives Canada = Bibliothèque et Archives Canada ISBN 13 :9780494021828 Total Pages :130 pages Book Rating :4.0/5 (218 download)
Book Synopsis Analyzing High-dimensional Microarray Data Using Variational-SOM [microform] by : Zhang, Linghai
Download or read book Analyzing High-dimensional Microarray Data Using Variational-SOM [microform] written by Zhang, Linghai and published by Library and Archives Canada = Bibliothèque et Archives Canada. This book was released on 2005 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis focuses on analyzing high-dimensional microarray data using the proposed algorithm, Variational-SOM. The original Self-Organizing Map (SOM) algorithm is an unsupervised neural network method and can be used to reduce the dimensionality of microarray data. The main disadvantage of SOM is that the topology of the map must be fixed from the beginning. In order to solve the problem, the Variational-SOM, of which the map's topology is determined dynamically, is proposed. The DNA microarray technology makes it possible to monitor expression levels of thousands of genes simultaneously. However, these data are of little use unless we are able to analyze them. Experimental results show that the Variational-SOM can reduce the dimensionality of data according to the information that the data contains and help to extract biological significance from the data. The analysis using Variational-SOM can produce more well-separated clusters with respect to clinical information than using the original SOM.
Book Synopsis Exploration and Analysis of DNA Microarray and Other High-Dimensional Data by : Dhammika Amaratunga
Download or read book Exploration and Analysis of DNA Microarray and Other High-Dimensional Data written by Dhammika Amaratunga and published by Wiley. This book was released on 2014-01-31 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis High-dimensional Microarray Data Analysis by : Shuichi Shinmura
Download or read book High-dimensional Microarray Data Analysis written by Shuichi Shinmura and published by Springer. This book was released on 2019-05-14 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how to decompose high-dimensional microarrays into small subspaces (Small Matryoshkas, SMs), statistically analyze them, and perform cancer gene diagnosis. The information is useful for genetic experts, anyone who analyzes genetic data, and students to use as practical textbooks. Discriminant analysis is the best approach for microarray consisting of normal and cancer classes. Microarrays are linearly separable data (LSD, Fact 3). However, because most linear discriminant function (LDF) cannot discriminate LSD theoretically and error rates are high, no one had discovered Fact 3 until now. Hard-margin SVM (H-SVM) and Revised IP-OLDF (RIP) can find Fact3 easily. LSD has the Matryoshka structure and is easily decomposed into many SMs (Fact 4). Because all SMs are small samples and LSD, statistical methods analyze SMs easily. However, useful results cannot be obtained. On the other hand, H-SVM and RIP can discriminate two classes in SM entirely. RatioSV is the ratio of SV distance and discriminant range. The maximum RatioSVs of six microarrays is over 11.67%. This fact shows that SV separates two classes by window width (11.67%). Such easy discrimination has been unresolved since 1970. The reason is revealed by facts presented here, so this book can be read and enjoyed like a mystery novel. Many studies point out that it is difficult to separate signal and noise in a high-dimensional gene space. However, the definition of the signal is not clear. Convincing evidence is presented that LSD is a signal. Statistical analysis of the genes contained in the SM cannot provide useful information, but it shows that the discriminant score (DS) discriminated by RIP or H-SVM is easily LSD. For example, the Alon microarray has 2,000 genes which can be divided into 66 SMs. If 66 DSs are used as variables, the result is a 66-dimensional data. These signal data can be analyzed to find malignancy indicators by principal component analysis and cluster analysis.
Book Synopsis Advanced Analysis Of Gene Expression Microarray Data by : Aidong Zhang
Download or read book Advanced Analysis Of Gene Expression Microarray Data written by Aidong Zhang and published by World Scientific Publishing Company. This book was released on 2006-06-27 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Analysis of Microarray Gene Expression Data by : Mei-Ling Ting Lee
Download or read book Analysis of Microarray Gene Expression Data written by Mei-Ling Ting Lee and published by Springer Science & Business Media. This book was released on 2007-05-08 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data.
Book Synopsis Methods of Microarray Data Analysis II by : Simon M. Lin
Download or read book Methods of Microarray Data Analysis II written by Simon M. Lin and published by Springer Science & Business Media. This book was released on 2007-05-08 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Methods of Microarray Data Analysis by : Simon M. Lin
Download or read book Methods of Microarray Data Analysis written by Simon M. Lin and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Methods of Microarray Data Analysis III by : Kimberly F. Johnson
Download or read book Methods of Microarray Data Analysis III written by Kimberly F. Johnson and published by Springer Science & Business Media. This book was released on 2007-05-08 with total page 247 pages. Available in PDF, EPUB and Kindle. Book excerpt: As microarray technology has matured, data analysis methods have advanced as well. Methods Of Microarray Data Analysis III is the third book in this pioneering series dedicated to the existing new field of microarrays. While initial techniques focused on classification exercises (volume I of this series), and later on pattern extraction (volume II of this series), this volume focuses on data quality issues. Problems such as background noise determination, analysis of variance, and errors in data handling are highlighted. Three tutorial papers are presented to assist with a basic understanding of underlying principles in microarray data analysis, and twelve new papers are highlighted analyzing the same CAMDA'02 datasets: the Project Normal data set or the Affymetrix Latin Square data set. A comparative study of these analytical methodologies brings to light problems, solutions and new ideas. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state of art of microarray data analysis.
Book Synopsis Statistical Analysis of Gene Expression Microarray Data by : Terry Speed
Download or read book Statistical Analysis of Gene Expression Microarray Data written by Terry Speed and published by CRC Press. This book was released on 2003-03-26 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 :Jennifer S. Shoemaker Publisher :Springer Science & Business Media ISBN 13 :0387230777 Total Pages :266 pages Book Rating :4.3/5 (872 download)
Book Synopsis Methods of Microarray Data Analysis IV by : Jennifer S. Shoemaker
Download or read book Methods of Microarray Data Analysis IV written by Jennifer S. Shoemaker and published by Springer Science & Business Media. This book was released on 2006-01-16 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt: As studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA conference plays a role in this evolving field by providing a forum in which investors can analyze the same data sets using different methods. Methods of Microarray Data Analysis IV is the fourth book in this series, and focuses on the important issue of associating array data with a survival endpoint. Previous books in this series focused on classification (Volume I), pattern recognition (Volume II), and quality control issues (Volume III). In this volume, four lung cancer data sets are the focus of analysis. We highlight three tutorial papers, including one to assist with a basic understanding of lung cancer, a review of survival analysis in the gene expression literature, and a paper on replication. In addition, 14 papers presented at the conference are included. This book is an excellent reference for academic and industrial researchers who want to keep abreast of the state of the art of microarray data analysis. Jennifer Shoemaker is a faculty member in the Department of Biostatistics and Bioinformatics and the Director of the Bioinformatics Unit for the Cancer and Leukemia Group B Statistical Center, Duke University Medical Center. Simon Lin is a faculty member in the Department of Biostatistics and Bioinformatics and the Manager of the Duke Bioinformatics Shared Resource, Duke University Medical Center.
Book Synopsis Microarray Data Analysis by : Michael J. Korenberg
Download or read book Microarray Data Analysis written by Michael J. Korenberg and published by Springer Science & Business Media. This book was released on 2008-02-03 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this new volume, renowned authors contribute fascinating, cutting-edge insights into microarray data analysis. Information on an array of topics is included in this innovative book including in-depth insights into presentations of genomic signal processing. Also detailed is the use of tiling arrays for large genomes analysis. The protocols follow the successful Methods in Molecular BiologyTM series format, offering step-by-step instructions, an introduction outlining the principles behind the technique, lists of the necessary equipment and reagents, and tips on troubleshooting and avoiding pitfalls.
Book Synopsis Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R by : Dan Lin
Download or read book Modeling Dose-Response Microarray Data in Early Drug Development Experiments Using R written by Dan Lin and published by Springer Science & Business Media. This book was released on 2012-08-27 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the analysis of dose-response microarray data in pharmaceutical settings, the goal being to cover this important topic for early drug development experiments and to provide user-friendly R packages that can be used to analyze this data. It is intended for biostatisticians and bioinformaticians in the pharmaceutical industry, biologists, and biostatistics/bioinformatics graduate students. Part I of the book is an introduction, in which we discuss the dose-response setting and the problem of estimating normal means under order restrictions. In particular, we discuss the pooled-adjacent-violator (PAV) algorithm and isotonic regression, as well as inference under order restrictions and non-linear parametric models, which are used in the second part of the book. Part II is the core of the book, in which we focus on the analysis of dose-response microarray data. Methodological topics discussed include: • Multiplicity adjustment • Test statistics and procedures for the analysis of dose-response microarray data • Resampling-based inference and use of the SAM method for small-variance genes in the data • Identification and classification of dose-response curve shapes • Clustering of order-restricted (but not necessarily monotone) dose-response profiles • Gene set analysis to facilitate the interpretation of microarray results • Hierarchical Bayesian models and Bayesian variable selection • Non-linear models for dose-response microarray data • Multiple contrast tests • Multiple confidence intervals for selected parameters adjusted for the false coverage-statement rate All methodological issues in the book are illustrated using real-world examples of dose-response microarray datasets from early drug development experiments.
Book Synopsis Microarray Data by : Shailaja R. Deshmukh
Download or read book Microarray Data written by Shailaja R. Deshmukh and published by Alpha Science International, Limited. This book was released on 2007 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: Functional Genomics, a branch of bioinformatics, is essentially an interdisciplinary subject in which biologists, statisticians and computer experts interact to analyze the microarray data. This book caters to the needs of all the three disciplines. For biologists and computer scientists, it explains concepts of statistics and statistical inference. For Biologists and Statisticians, it provides annotated R programs to analyze microarray data. For Statisticians and Computer scientists, it explains basics of biology relevant to microarray experiment. Thus, the book will be useful to scientists from all the three disciplines, with not much knowledge of other disciplines, to analyze microarray data and interpret the results.
Book Synopsis Microarray Gene Expression Data Analysis by : Helen Causton
Download or read book Microarray Gene Expression Data Analysis written by Helen Causton and published by John Wiley & Sons. This book was released on 2009-04-01 with total page 176 pages. Available in PDF, EPUB and Kindle. Book excerpt: This guide covers aspects of designing microarray experiments and analysing the data generated, including information on some of the tools that are available from non-commercial sources. Concepts and principles underpinning gene expression analysis are emphasised and wherever possible, the mathematics has been simplified. The guide is intended for use by graduates and researchers in bioinformatics and the life sciences and is also suitable for statisticians who are interested in the approaches currently used to study gene expression. Microarrays are an automated way of carrying out thousands of experiments at once, and allows scientists to obtain huge amounts of information very quickly Short, concise text on this difficult topic area Clear illustrations throughout Written by well-known teachers in the subject Provides insight into how to analyse the data produced from microarrays
Book Synopsis Analysis of Microarray Data by : Matthias Dehmer
Download or read book Analysis of Microarray Data written by Matthias Dehmer and published by John Wiley & Sons. This book was released on 2008-09-08 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents: * Understanding and Preprocessing Microarray Data * Clustering of Microarray Data * Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order * Bilayer Verification Algorithm * Probabilistic Boolean Networks as Models for Gene Regulation * Estimating Transcriptional Regulatory Networks by a Bayesian Network * Analysis of Therapeutic Compound Effects * Statistical Methods for Inference of Genetic Networks and Regulatory Modules * Identification of Genetic Networks by Structural Equations * Predicting Functional Modules Using Microarray and Protein Interaction Data * Integrating Results from Literature Mining and Microarray Experiments to Infer Gene Networks The book is for both, scientists using the technique as well as those developing new analysis techniques.
Book Synopsis Methods of Microarray Data Analysis V by : Patrick McConnell
Download or read book Methods of Microarray Data Analysis V written by Patrick McConnell and published by Springer Science & Business Media. This book was released on 2007-02-24 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is dedicated solely to the analysis of microarray data. Its unique approach of presenting different methods by analyzing the same data set shows the strengths and weakness of each method. Part of the book is devoted to review papers, which provide a more general look at various analytical approaches. It also presents some background readings for the advanced topics discussed in the CAMDA papers.
Book Synopsis Statistics for Microarrays by : Ernst Wit
Download or read book Statistics for Microarrays written by Ernst Wit and published by John Wiley & Sons. This book was released on 2004-07-23 with total page 286 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in microarrays has increased considerably in the last ten years. This increase in the use of microarray technology has led to the need for good standards of microarray experimental notation, data representation, and the introduction of standard experimental controls, as well as standard data normalization and analysis techniques. Statistics for Microarrays: Design, Analysis and Inference is the first book that presents a coherent and systematic overview of statistical methods in all stages in the process of analysing microarray data – from getting good data to obtaining meaningful results. Provides an overview of statistics for microarrays, including experimental design, data preparation, image analysis, normalization, quality control, and statistical inference. Features many examples throughout using real data from microarray experiments. Computational techniques are integrated into the text. Takes a very practical approach, suitable for statistically-minded biologists. Supported by a Website featuring colour images, software, and data sets. Primarily aimed at statistically-minded biologists, bioinformaticians, biostatisticians, and computer scientists working with microarray data, the book is also suitable for postgraduate students of bioinformatics.
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