Exploration and Analysis of DNA Microarray and Other High-Dimensional Data, Second Edition, Amaratunga
Автор: Zhang Aidong Название: Advanced analysis of gene expression microarray data ISBN: 9812566457 ISBN-13(EAN): 9789812566454 Издательство: World Scientific Publishing Рейтинг: Цена: 8981.00 р. 12830.00-30% Наличие на складе: Есть (4 шт.) Описание: Focuses on the development and application of the advanced data mining, machine learning, and visualization techniques for the identification of significant patterns in gene expression microarray data. This book is suitable for biomedical researchers for learning the methods for analyzing gene expression microarray data.
Автор: Shuichi Shinmura Название: High-dimensional Microarray Data Analysis ISBN: 9811359970 ISBN-13(EAN): 9789811359972 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Agapito Название: Microarray Data Analysis ISBN: 1071618415 ISBN-13(EAN): 9781071618417 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This meticulous book explores the leading methodologies, techniques, and tools for microarray data analysis, given the difficulty of harnessing the enormous amount of data. The book includes examples and code in R, requiring only an introductory computer science understanding, and the structure and the presentation of the chapters make it suitable for use in bioinformatics courses. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of key detail and expert implementation advice that ensures successful results and reproducibility. Authoritative and practical, Microarray Data Analysis is an ideal guide for students or researchers who need to learn the main research topics and practitioners who continue to work with microarray datasets.
Описание: 1 New Theory of Discriminant Analysis.- 1.1 Introduction.- 1.2 Motivation for our Research.- 1.3 Discriminant Functions.- 1.4 Unresolved Problem (Problem 1).- 1.5 LSD Discrimination (Problem 2).- 1.6 Generalized Inverse Matrices (Problem 3).- 1.7 K-fold Cross-validation (Problem 4).- 1.8 Matroska Feature Selection Method (Problem 5) .- 1.9 Summary.- References.- 2 Iris Data and Fisher's Assumption.- 2.1 Introduction.- 2.2 Iris Data.- 2.3 Comparison of Seven LDFs.- 2.4 100-folf Cross-validation for Small Sample Method (Method 1).- 2.5 Summary.- References.- 3 The Cephalo-Pelvic Disproportion (CPD) Data with Collinearity.- 3.1 Introduction.- 3.2 CPD Data.- 3.3 100-folf Cross-validation.- 3.4 Trial to Remove Collinearity.- 3.5 Summary.- References.- 4 Student Data and Problem 1.- 4.1 Introduction.- 4.2 Student Data.- 4.3 100-folf Cross-validation for Student Data.- 4.4 Student Linearly Separable Data.- 4.5 Summary.- References.- 5 The Pass/Fail Determination using Exam Scores -A Trivial Linear Discriminant Function.- 5.1 Introduction.- 5.2 Pass/Fail Determination by Exam Scores Data in 2012.- 5.3 Pass/Fail Determination by Exam Scores (50% Level in 2012).- 5.4 Pass/Fail Determination by Exam Scores (90% Level in 2012).- 5.5 Pass/Fail Determination by Exam Scores (10% Level in 2012).- 5.6 Summary.- 6 Best Model for the Swiss Banknote Data - Explanation 1 of Matroska Feature -selection Method (Method 2) -. References.- 6 Best Model for Swiss Banknote Data.- 6.1 Introduction.- 6.2 Swiss Banknote Data.- 6.3 100-folf Cross-validation for Small Sample Method.- 6.4 Explanation 1 for Swiss Banknote Data.- 6.5 Summary.- References.- 7 Japanese Automobile Data - Explanation 2 of Matroska Feature Selection Method (Method 2).- 7.1 Introduction.- 7.2 Japanese Automobile Data.- 7.3 100-folf Cross-validation (Method 1).- 7.4 Matroska Feature Selection Method (Method 2).- 7.5 Summary.- References.- 8 Matroska Feature Selection Method for Microarray Data (Method 2).- 8.1 Introduction.- 8.2 Matroska Feature Selection Method (Method2).- 8.3 Results of the Golub et al. Dataset.- 8.4 How to Analyze the First BGS.- 8.5 Statistical Analysis of SM1.- 8.6 Summary.- References.- 9 LINGO Program 1 of Method 1.- 9.1 Introduction.- 9.2 Natural (Mathematical) Notation by LINGO.- 9.3 Iris Data in Excel.- 9.4 Six LDFs by LINGO.- 9.5 Discrimination of Iris Data by LINGO.- 9.6 How to Generate Re-sampling Samples and Prepare Data in Excel File.- 9.7 Set Model by LINGO.- Index.
Автор: Mei-Ling Ting Lee Название: Analysis of Microarray Gene Expression Data ISBN: 1475788231 ISBN-13(EAN): 9781475788235 Издательство: Springer Рейтинг: Цена: 20962.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. 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.
Автор: Michael J. Korenberg Название: Microarray Data Analysis ISBN: 1627039090 ISBN-13(EAN): 9781627039093 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
Автор: Rueda, Luis Название: Microarray Image and Data Analysis ISBN: 1466586826 ISBN-13(EAN): 9781466586826 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Нет в наличии.
Автор: Agapito Giuseppe Название: Microarray Data Analysis ISBN: 1071618385 ISBN-13(EAN): 9781071618387 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This meticulous book explores the leading methodologies, techniques, and tools for microarray data analysis, given the difficulty of harnessing the enormous amount of data. The book includes examples and code in R, requiring only an introductory computer science understanding, and the structure and the presentation of the chapters make it suitable for use in bioinformatics courses. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of key detail and expert implementation advice that ensures successful results and reproducibility. Authoritative and practical, Microarray Data Analysis is an ideal guide for students or researchers who need to learn the main research topics and practitioners who continue to work with microarray datasets.
Автор: Simon M. Lin; Kimberly F. Johnson Название: Methods of Microarray Data Analysis ISBN: 1461352819 ISBN-13(EAN): 9781461352815 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Papers from CAMDA 2000, December 18-19, 2000, Duke University, Durham, NC, USA
Автор: Shuichi Shinmura Название: New Theory of Discriminant Analysis After R. Fisher ISBN: 9811021635 ISBN-13(EAN): 9789811021633 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 1 New Theory of Discriminant Analysis.- 1.1 Introduction.- 1.2 Motivation for our Research.- 1.3 Discriminant Functions.- 1.4 Unresolved Problem (Problem 1).- 1.5 LSD Discrimination (Problem 2).- 1.6 Generalized Inverse Matrices (Problem 3).- 1.7 K-fold Cross-validation (Problem 4).- 1.8 Matroska Feature Selection Method (Problem 5) .- 1.9 Summary.- References.- 2 Iris Data and Fisher's Assumption.- 2.1 Introduction.- 2.2 Iris Data.- 2.3 Comparison of Seven LDFs.- 2.4 100-folf Cross-validation for Small Sample Method (Method 1).- 2.5 Summary.- References.- 3 The Cephalo-Pelvic Disproportion (CPD) Data with Collinearity.- 3.1 Introduction.- 3.2 CPD Data.- 3.3 100-folf Cross-validation.- 3.4 Trial to Remove Collinearity.- 3.5 Summary.- References.- 4 Student Data and Problem 1.- 4.1 Introduction.- 4.2 Student Data.- 4.3 100-folf Cross-validation for Student Data.- 4.4 Student Linearly Separable Data.- 4.5 Summary.- References.- 5 The Pass/Fail Determination using Exam Scores -A Trivial Linear Discriminant Function.- 5.1 Introduction.- 5.2 Pass/Fail Determination by Exam Scores Data in 2012.- 5.3 Pass/Fail Determination by Exam Scores (50% Level in 2012).- 5.4 Pass/Fail Determination by Exam Scores (90% Level in 2012).- 5.5 Pass/Fail Determination by Exam Scores (10% Level in 2012).- 5.6 Summary.- 6 Best Model for the Swiss Banknote Data - Explanation 1 of Matroska Feature -selection Method (Method 2) -. References.- 6 Best Model for Swiss Banknote Data.- 6.1 Introduction.- 6.2 Swiss Banknote Data.- 6.3 100-folf Cross-validation for Small Sample Method.- 6.4 Explanation 1 for Swiss Banknote Data.- 6.5 Summary.- References.- 7 Japanese Automobile Data - Explanation 2 of Matroska Feature Selection Method (Method 2).- 7.1 Introduction.- 7.2 Japanese Automobile Data.- 7.3 100-folf Cross-validation (Method 1).- 7.4 Matroska Feature Selection Method (Method 2).- 7.5 Summary.- References.- 8 Matroska Feature Selection Method for Microarray Data (Method 2).- 8.1 Introduction.- 8.2 Matroska Feature Selection Method (Method2).- 8.3 Results of the Golub et al. Dataset.- 8.4 How to Analyze the First BGS.- 8.5 Statistical Analysis of SM1.- 8.6 Summary.- References.- 9 LINGO Program 1 of Method 1.- 9.1 Introduction.- 9.2 Natural (Mathematical) Notation by LINGO.- 9.3 Iris Data in Excel.- 9.4 Six LDFs by LINGO.- 9.5 Discrimination of Iris Data by LINGO.- 9.6 How to Generate Re-sampling Samples and Prepare Data in Excel File.- 9.7 Set Model by LINGO.- Index.
Автор: Simon M. Lin (Editor), Kimberly F. Johnson Название: Methods of Microarray Data Analysis II: Papers from CAMDA `01 ISBN: 1475788312 ISBN-13(EAN): 9781475788310 Издательство: Springer Рейтинг: Цена: 13974.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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. Methods of Microarray Data Analysis II focuses on a single data set, using a different method of analysis in each chapter.
Автор: Patrick McConnell; Simon Lin; Patrick Hurban Название: Methods of Microarray Data Analysis V ISBN: 1441941797 ISBN-13(EAN): 9781441941794 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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.
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