High Dimensional Microarray Data Analysis

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High-dimensional Microarray Data Analysis

Shuichi Shinmura
High dimensional Microarray Data Analysis Book PDF

Author : Shuichi Shinmura
Publisher : Springer
Page : 419 pages
File Size : 47,9 Mb
Release : 2019-05-14
Category : Medical
ISBN : 9789811359989

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High-dimensional Microarray Data Analysis by Shuichi Shinmura Pdf

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.

Exploration and Analysis of DNA Microarray and Other High-Dimensional Data

Dhammika Amaratunga,Javier Cabrera,Ziv Shkedy
Exploration and Analysis of DNA Microarray and Other High Dimensional Data Book PDF

Author : Dhammika Amaratunga,Javier Cabrera,Ziv Shkedy
Publisher : John Wiley & Sons
Page : 320 pages
File Size : 54,6 Mb
Release : 2014-01-27
Category : Mathematics
ISBN : 9781118364529

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Exploration and Analysis of DNA Microarray and Other High-Dimensional Data by Dhammika Amaratunga,Javier Cabrera,Ziv Shkedy Pdf

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.

High-Dimensional Data Analysis in Cancer Research

Xiaochun Li,Ronghui Xu
High Dimensional Data Analysis in Cancer Research Book PDF

Author : Xiaochun Li,Ronghui Xu
Publisher : Springer Science & Business Media
Page : 164 pages
File Size : 45,9 Mb
Release : 2008-12-19
Category : Medical
ISBN : 9780387697659

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High-Dimensional Data Analysis in Cancer Research by Xiaochun Li,Ronghui Xu Pdf

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

Advanced Analysis Of Gene Expression Microarray Data

Aidong Zhang
Advanced Analysis Of Gene Expression Microarray Data Book PDF

Author : Aidong Zhang
Publisher : World Scientific Publishing Company
Page : 356 pages
File Size : 47,7 Mb
Release : 2006-06-27
Category : Science
ISBN : 9789813106642

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Advanced Analysis Of Gene Expression Microarray Data by Aidong Zhang Pdf

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.

Microarray Image and Data Analysis

Luis Rueda
Microarray Image and Data Analysis Book PDF

Author : Luis Rueda
Publisher : CRC Press
Page : 520 pages
File Size : 43,6 Mb
Release : 2018-09-03
Category : Science
ISBN : 9781466586871

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Microarray Image and Data Analysis by Luis Rueda Pdf

Microarray Image and Data Analysis: Theory and Practice is a compilation of the latest and greatest microarray image and data analysis methods from the multidisciplinary international research community. Delivering a detailed discussion of the biological aspects and applications of microarrays, the book: Describes the key stages of image processing, gridding, segmentation, compression, quantification, and normalization Features cutting-edge approaches to clustering, biclustering, and the reconstruction of regulatory networks Covers different types of microarrays such as DNA, protein, tissue, and low- and high-density oligonucleotide arrays Examines the current state of various microarray technologies, including their availability and affordability Explains how data generated by microarray experiments are analyzed to obtain meaningful biological conclusions An essential reference for academia and industry, Microarray Image and Data Analysis: Theory and Practice provides readers with valuable tools and techniques that extend to a wide range of biological studies and microarray platforms.

DNA Microarrays and Related Genomics Techniques

David B. Allison,Grier P. Page,T. Mark Beasley,Jode W. Edwards
DNA Microarrays and Related Genomics Techniques Book PDF

Author : David B. Allison,Grier P. Page,T. Mark Beasley,Jode W. Edwards
Publisher : CRC Press
Page : 391 pages
File Size : 44,7 Mb
Release : 2005-11-14
Category : Mathematics
ISBN : 9781420028799

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DNA Microarrays and Related Genomics Techniques by David B. Allison,Grier P. Page,T. Mark Beasley,Jode W. Edwards Pdf

Considered highly exotic tools as recently as the late 1990s, microarrays are now ubiquitous in biological research. Traditional statistical approaches to design and analysis were not developed to handle the high-dimensional, small sample problems posed by microarrays. In just a few short years the number of statistical papers providing approaches

Statistical Methods for Microarray Data Analysis

Andrei Y. Yakovlev,Lev Klebanov,Daniel Gaile
Statistical Methods for Microarray Data Analysis Book PDF

Author : Andrei Y. Yakovlev,Lev Klebanov,Daniel Gaile
Publisher : Unknown
Page : 212 pages
File Size : 43,9 Mb
Release : 2013
Category : DNA microarrays
ISBN : 1603273379

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Statistical Methods for Microarray Data Analysis by Andrei Y. Yakovlev,Lev Klebanov,Daniel Gaile Pdf

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 Biology 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.

Data Mining and Bioinformatics

Mehmet M Dalkilic
Data Mining and Bioinformatics Book PDF

Author : Mehmet M Dalkilic
Publisher : Springer Science & Business Media
Page : 204 pages
File Size : 42,7 Mb
Release : 2006-12-21
Category : Computers
ISBN : 9783540689706

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Data Mining and Bioinformatics by Mehmet M Dalkilic Pdf

This book constitutes the thoroughly refereed post-proceedings of the First VLDB 2006 International Workshop on Data Mining and Bioinformatics, VDMB 2006, held in Seoul, Korea in September 2006 in conjunction with VLDB 2006. The 15 revised full papers cover various topics in the areas of microarray data analysis, bioinformatics system and text retrieval, application of gene expression data, and sequence analysis.

Methods of Microarray Data Analysis III

Kimberly F. Johnson,Simon M. Lin
Methods of Microarray Data Analysis III Book PDF

Author : Kimberly F. Johnson,Simon M. Lin
Publisher : Springer Science & Business Media
Page : 247 pages
File Size : 40,9 Mb
Release : 2003-09-30
Category : Science
ISBN : 9781402075827

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Methods of Microarray Data Analysis III by Kimberly F. Johnson,Simon M. Lin Pdf

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.

Methods of Microarray Data Analysis

Simon M. Lin,Kimberly F. Johnson
Methods of Microarray Data Analysis Book PDF

Author : Simon M. Lin,Kimberly F. Johnson
Publisher : Springer Science & Business Media
Page : 192 pages
File Size : 42,7 Mb
Release : 2012-12-06
Category : Science
ISBN : 9781461508731

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Methods of Microarray Data Analysis by Simon M. Lin,Kimberly F. Johnson Pdf

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.

Analyzing High-dimensional Microarray Data Using Variational-SOM [microform]

Zhang, Linghai
Analyzing High dimensional Microarray Data Using Variational SOM microform Book PDF

Author : Zhang, Linghai
Publisher : Library and Archives Canada = Bibliothèque et Archives Canada
Page : 130 pages
File Size : 49,9 Mb
Release : 2005
Category : Electronic
ISBN : 0494021829

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Analyzing High-dimensional Microarray Data Using Variational-SOM [microform] by Zhang, Linghai Pdf

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.

Gene Expression Data Analysis

Pankaj Barah,Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Gene Expression Data Analysis Book PDF

Author : Pankaj Barah,Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita
Publisher : CRC Press
Page : 276 pages
File Size : 41,6 Mb
Release : 2021-11-08
Category : Computers
ISBN : 9781000425758

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Gene Expression Data Analysis by Pankaj Barah,Dhruba Kumar Bhattacharyya,Jugal Kumar Kalita Pdf

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 the biological sciences

Feature Selection for High-Dimensional Data

Verónica Bolón-Canedo,Noelia Sánchez-Maroño,Amparo Alonso-Betanzos
Feature Selection for High Dimensional Data Book PDF

Author : Verónica Bolón-Canedo,Noelia Sánchez-Maroño,Amparo Alonso-Betanzos
Publisher : Springer
Page : 147 pages
File Size : 46,7 Mb
Release : 2015-10-05
Category : Computers
ISBN : 9783319218588

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Feature Selection for High-Dimensional Data by Verónica Bolón-Canedo,Noelia Sánchez-Maroño,Amparo Alonso-Betanzos Pdf

This book offers a coherent and comprehensive approach to feature subset selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection for high-dimensional data. The authors first focus on the analysis and synthesis of feature selection algorithms, presenting a comprehensive review of basic concepts and experimental results of the most well-known algorithms. They then address different real scenarios with high-dimensional data, showing the use of feature selection algorithms in different contexts with different requirements and information: microarray data, intrusion detection, tear film lipid layer classification and cost-based features. The book then delves into the scenario of big dimension, paying attention to important problems under high-dimensional spaces, such as scalability, distributed processing and real-time processing, scenarios that open up new and interesting challenges for researchers. The book is useful for practitioners, researchers and graduate students in the areas of machine learning and data mining.

Methods of Microarray Data Analysis IV

Jennifer S. Shoemaker,Simon M. Lin
Methods of Microarray Data Analysis IV Book PDF

Author : Jennifer S. Shoemaker,Simon M. Lin
Publisher : Springer Science & Business Media
Page : 266 pages
File Size : 40,5 Mb
Release : 2006-01-16
Category : Medical
ISBN : 9780387230771

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Methods of Microarray Data Analysis IV by Jennifer S. Shoemaker,Simon M. Lin Pdf

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.

Statistical Analysis of Gene Expression Microarray Data

Terry Speed
Statistical Analysis of Gene Expression Microarray Data Book PDF

Author : Terry Speed
Publisher : CRC Press
Page : 237 pages
File Size : 40,6 Mb
Release : 2003-03-26
Category : Mathematics
ISBN : 9780203011232

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Statistical Analysis of Gene Expression Microarray Data by Terry Speed Pdf

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