Methods Of Microarray Data Analysis V
DOWNLOAD
Download Methods Of Microarray Data Analysis V PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Methods Of Microarray Data Analysis V book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page
Methods Of Microarray Data Analysis V
DOWNLOAD
Author : Patrick McConnell
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-02-24
Methods Of Microarray Data Analysis V written by Patrick McConnell and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-02-24 with Science categories.
As studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA (Critical Assessment of Microarray Data Analysis) conference was the first to establish a forum for a cross section of researchers to look at a common data set and apply innovative analytical techniques to microarray data. Methods of Microarray Analysis V includes selected papers from CAMDA'04, and focuses on data sets relating to a significant global health issue, malaria. Previous books focused on classification (V. I), pattern recognition (V. II), quality control issues (V. III), and associating array data with a survival endpoint, lung cancer, (V. IV). The contributions come from research fields including statistics, biology, computer science and mathematics. 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.
Methods Of Microarray Data Analysis
DOWNLOAD
Author : Simon M. Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2002
Methods Of Microarray Data Analysis written by Simon M. Lin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Mathematics categories.
Papers from CAMDA 2000, December 18-19, 2000, Duke University, Durham, NC, USA
Methods Of Microarray Data Analysis V
DOWNLOAD
Author : Patrick McConnell
language : en
Publisher: Springer
Release Date : 2006-11-07
Methods Of Microarray Data Analysis V written by Patrick McConnell and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-11-07 with Science categories.
As studies using microarray technology have evolved, so have the data analysis methods used to analyze these experiments. The CAMDA (Critical Assessment of Microarray Data Analysis) conference was the first to establish a forum for a cross section of researchers to look at a common data set and apply innovative analytical techniques to microarray data. Methods of Microarray Analysis V includes selected papers from CAMDA'04, and focuses on data sets relating to a significant global health issue, malaria. Previous books focused on classification (V. I), pattern recognition (V. II), quality control issues (V. III), and associating array data with a survival endpoint, lung cancer, (V. IV). The contributions come from research fields including statistics, biology, computer science and mathematics. 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.
Methods Of Microarray Data Analysis Ii
DOWNLOAD
Author : Simon M. Lin
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-05-08
Methods Of Microarray Data Analysis Ii written by Simon M. Lin and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-05-08 with Science categories.
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.
Methods Of Microarray Data Analysis Iii
DOWNLOAD
Author : Kimberly F. Johnson
language : en
Publisher: Springer Science & Business Media
Release Date : 2003-09-30
Methods Of Microarray Data Analysis Iii written by Kimberly F. Johnson and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-09-30 with Science categories.
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.
Statistical Methods For Microarray Data Analysis
DOWNLOAD
Author : Andrei Y. Yakovlev
language : en
Publisher: Humana Press
Release Date : 2013-02-06
Statistical Methods For Microarray Data Analysis written by Andrei Y. Yakovlev and has been published by Humana Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-02-06 with Medical categories.
Microarrays for simultaneous measurement of redundancy of RNA species are used in fundamental biology as well as in medical research. Statistically,a microarray may be considered as an observation of very high dimensionality equal to the number of expression levels measured on it. In Statistical Methods for Microarray Data Analysis: Methods and Protocols, expert researchers in the field detail many methods and techniques used to study microarrays, guiding the reader from microarray technology to statistical problems of specific multivariate data analysis. Written in the highly successful Methods in Molecular BiologyTM series format, the chapters include the kind of detailed description and implementation advice that is crucial for getting optimal results in the laboratory. Thorough and intuitive, Statistical Methods for Microarray Data Analysis: Methods and Protocols aids scientists in continuing to study microarrays and the most current statistical methods.
Methods Of Microarray Data Analysis Iv
DOWNLOAD
Author : Jennifer S. Shoemaker
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-01-16
Methods Of Microarray Data Analysis Iv written by Jennifer S. Shoemaker and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-16 with Medical categories.
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.
Microarray Data Analysis
DOWNLOAD
Author : Michael J. Korenberg
language : en
Publisher: Springer Science & Business Media
Release Date : 2008-02-03
Microarray Data Analysis written by Michael J. Korenberg and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-02-03 with Science categories.
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.
A Practical Approach To Microarray Data Analysis
DOWNLOAD
Author : Daniel P. Berrar
language : en
Publisher: Springer Science & Business Media
Release Date : 2002-12-31
A Practical Approach To Microarray Data Analysis written by Daniel P. Berrar and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-12-31 with Science categories.
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.
Gene Expression Data Analysis
DOWNLOAD
Author : Pankaj Barah
language : en
Publisher: CRC Press
Release Date : 2021-11-21
Gene Expression Data Analysis written by Pankaj Barah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-21 with Computers categories.
Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences