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Microarray Innovations, 


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Название:  Microarray Innovations
ISBN: 9780367385811
Издательство: Taylor&Francis
Классификация:



ISBN-10: 0367385813
Обложка/Формат: Paperback
Страницы: 360
Вес: 0.67 кг.
Дата издания: 20.12.2019
Язык: English
Размер: 252 x 175 x 20
Читательская аудитория: Tertiary education (us: college)
Основная тема: Drug Discovery
Подзаголовок: Technology and Experimentation
Ссылка на Издательство: Link
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Поставляется из: Европейский союз
Описание:

In recent years, high-density DNA microarrays have revolutionized biomedical research and drug discovery efforts by the pharmaceutical industry. Their efficacy in identifying and prioritizing drug targets based on their ability to confirm a large number of gene expression measurements in parallel has become a key element in drug discovery. Microarray Innovations: Technology and Experimentation examines the incredibly powerful nature of array technology and the ways in which it can be applied to understanding the genomic basis of disease.





Explores a myriad of applications in use today



This volume explores recent innovations in the microarray field and tracks the evolution of the major platforms currently used. The international panel of contributors presents a survey of the past five years research and advancements in microarray methods and applications and their usage in drug discovery and biomedical research. The contributions discuss improvements in automation (array fabrication and hybridization), new substrates for printing arrays, platform comparisons and contrasts, experimental design, and data normalization and mining schemes. They also review epigenomic array studies, electronic microarrays, comparative genomic hybridization, microRNA arrays, and mutational analyzes. In addition, the book provides coverage of important clinical diagnostic arrays, protein arrays, and neuroscience applications.



Examines improved methodologies





As microarrays have evolved steadily over time from archetypical in-house complementary DNA (cDNA) arrays to robust commercial oligonucleotide platforms, there has been a migration to higher density biochips with increasing content and better analytical methodologies. This compendium summarizes the vast advances that have been made in this technology, highlighting the supreme advantages of microarray-based appro



Advanced analysis of gene expression microarray data

Автор: Zhang Aidong
Название: Advanced analysis of gene expression microarray data
ISBN: 9812566457 ISBN-13(EAN): 9789812566454
Издательство: World Scientific Publishing
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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.

A Practical Approach to Microarray Data Analysis

Автор: Berrar Daniel P., Dubitzky Werner, Granzow Martin
Название: A Practical Approach to Microarray Data Analysis
ISBN: 1402072600 ISBN-13(EAN): 9781402072604
Издательство: Springer
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Цена: 4890.00 р. 6986.00 -30%
Наличие на складе: Есть (1 шт.)
Описание: A Practical Approach to Microarray Data Analysis is for all life scientists, statisticians, computer experts, technology developers, managers, and other professionals tasked with developing, deploying, and using microarray technology including the necessary computational infrastructure and analytical tools. The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools. It is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. The book is designed to be used by the practicing professional tasked with the design and analysis of microarray experiments or as a text for a senior undergraduate- or graduate level course in analytical genetics, biology, bioinformatics, computational biology, statistics and data mining, or applied computer science. Key topics covered include: -Format of result from data analysis, analytical modeling/experimentation; -Validation of analytical results; -Data analysis/Modeling task; -Analysis/modeling tools; -Scientific questions, goals, and tasks; -Application; -Data analysis methods; -Criteria for assessing analysis methodologies, models, and tools.

Analysis of Microarray Gene Expression Data

Автор: Mei-Ling Ting Lee
Название: Analysis of Microarray Gene Expression Data
ISBN: 1475788231 ISBN-13(EAN): 9781475788235
Издательство: Springer
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Цена: 20962.00 р.
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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.

Methods of Microarray Data Analysis III

Автор: Kimberly F. Johnson; Simon M. Lin
Название: Methods of Microarray Data Analysis III
ISBN: 1475785046 ISBN-13(EAN): 9781475785043
Издательство: Springer
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Цена: 6986.00 р.
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Описание: 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.

Design and Analysis of DNA Microarray Investigations

Автор: Simon
Название: Design and Analysis of DNA Microarray Investigations
ISBN: 0387001352 ISBN-13(EAN): 9780387001357
Издательство: Springer
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Цена: 20896.00 р.
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Описание: This book discusses the analysis of gene expression profile data from DNA micorarray studies and the design of such studies. The book will address design and analysis issues for both of the major classes of DNA microarrays, the cDNA microarrays and the oligonucleotide arrays. DNA microarrays are a new technology that is revolutionizing biological and biomedical research.

Most biologists are trying to analyze their own data using a variety of commercial and public domain software. This book will provide an authoritative review of the methods available and present it in a manner that is intelligible to biologists.

Microarray Analysis

Автор: Mark Schena
Название: Microarray Analysis
ISBN: 0471414433 ISBN-13(EAN): 9780471414438
Издательство: Wiley
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Цена: 26136.00 р.
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Описание: * Provides a working knowledge of applications to a broad spectrum of disciplines.
* Includes coverage of bioinformatics, novel arrays, and applications for clinical studies.
* Supplemented with a separate chapter on the business of the technology.

Microarray Innovations

Автор: Hardiman, Gary
Название: Microarray Innovations
ISBN: 1420094483 ISBN-13(EAN): 9781420094480
Издательство: Taylor&Francis
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Цена: 27562.00 р.
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Microarray Data Analysis

Автор: Michael J. Korenberg
Название: Microarray Data Analysis
ISBN: 1627039090 ISBN-13(EAN): 9781627039093
Издательство: Springer
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Цена: 18167.00 р.
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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.

New Theory of Discriminant Analysis After R. Fisher: Advanced Research by the Feature Selection Method for Microarray Data

Автор: Shinmura Shuichi
Название: New Theory of Discriminant Analysis After R. Fisher: Advanced Research by the Feature Selection Method for Microarray Data
ISBN: 9811095469 ISBN-13(EAN): 9789811095467
Издательство: Springer
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Цена: 13974.00 р.
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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.

Microarray Technology Through Applications

Автор: Falciani, Francesco
Название: Microarray Technology Through Applications
ISBN: 0415378532 ISBN-13(EAN): 9780415378536
Издательство: Taylor&Francis
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Цена: 9492.00 р.
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High-dimensional Microarray Data Analysis

Автор: Shuichi Shinmura
Название: High-dimensional Microarray Data Analysis
ISBN: 9811359970 ISBN-13(EAN): 9789811359972
Издательство: Springer
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Цена: 13974.00 р.
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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.

Methods in Microarray Normalization

Автор: Stafford, Phillip
Название: Methods in Microarray Normalization
ISBN: 1420052780 ISBN-13(EAN): 9781420052787
Издательство: Taylor&Francis
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Цена: 16078.00 р.
Наличие на складе: Поставка под заказ.


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