Microarray Data Analysis, Agapito Giuseppe

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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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: 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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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
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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: Daniel P. Berrar; Werner Dubitzky; Martin Granzow
Название: A Practical Approach to Microarray Data Analysis
ISBN: 1441912266 ISBN-13(EAN): 9781441912268
Издательство: Springer
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Описание: The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools.

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.

Автор: Patrick McConnell; Simon Lin; Patrick Hurban
Название: Methods of Microarray Data Analysis V
ISBN: 1441941797 ISBN-13(EAN): 9781441941794
Издательство: Springer
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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.

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

Автор: Simon M. Lin; Kimberly F. Johnson
Название: Methods of Microarray Data Analysis
ISBN: 1461352819 ISBN-13(EAN): 9781461352815
Издательство: Springer
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Описание: Papers from CAMDA 2000, December 18-19, 2000, Duke University, Durham, NC, USA

Автор: Guzzi Pietro Hiram
Название: Microarray Data Analysis: Methods and Applications
ISBN: 1493979930 ISBN-13(EAN): 9781493979936
Издательство: Springer
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Цена: 19564.00 р.
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Описание:

Di Wu and Michael P. Gantier

2) Methods and Techniques for miRNA Data Analysis

Francesca Cristiano and Pierangelo Veltri

3) Bioinformatics and Microarray Data Analysis on The Cloud

Barbara Calabrese and Mario Cannataro

4) Classification and Clustering on Microarray Data for Gene Functional Prediction using R

Liliana Lуpez Kleine, Rosa Montaсo, and Francisco Torres-Avilйs

5) Querying co-regulated genes on diverse gene expression datasets via biclustering

Mehmet Deveci, Onur Kucuktunc, Kernal Eren, Doruk Bozadag, Kamer Kaya, and Umit V. Catalyurek

6) MetaMirClust: Discovery and Exploration of Evolutionarily Conserved miRNA Clusters

Wen-Ching Chan and Wen-chang Lin

7) Analysis of Gene Expression Patterns using Biclustering

Swarup Roy, Dhruba K Bhattacharyya, and Jugal K Kalita

8) Using semantic Similarities and csbl.go for Analyzing Microarray Data

Kristian Ovaska

9) Ontology Based Analysis of Microarray Data

Giuseppe Agapito and Marianna Milano

10) Integrated Analysis of Transcriptomic and Proteomic Datasets Reveals Information on Protein Expressivity and Factors Affecting Translational Efficiency

Jiangxin Wang, Gang Wu, Lei Chen, and Weiwen Zhang

11) Integrating Microarray Data and GRNs

L. Koumakis, G. Potamias, M. Tsiknakis, M. Zervakis, and V. Moustakis

12) Biological Network Inference from Microarray Data, Current Solutions and Assessments

Swarup Roy and Pietro Hiram Guzzi

13) A Protocol to Collect Specific Mouse Skeletal Muscles for Metabolomics Studies

Zhuohui Gan, Zhenxing Fu, Jennifer C. Stowe, Frank L. Powell, and Andrew D. McCulloch

14) Functional Analysis of microRNA in Multiple Myeloma

Maria Teresa Di Martino, Nicola Amodio, Pierfrancesco Tassone, and Pierosandro Tagliaferri

15) Microarray Analysis in Glioblastomas

Kaumudi M. Bhawe and Manish K. Aghi

16) Analysis of microRNA Microarrays in Cardiogenesis

Diego Franco, Fernando Bonet, Francisco Hernandez-Torres, Estefania Lozano-Velasco, Francisco Esteban, and Amelia E Aranega

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