Bioinformatics Database Systems
Modern biological databases comprise not only data, but also sophisticated query facilities and bioinformatics data analysis tools. This book provides an exploration through the world of Bioinformatics Database Systems.

The book summarizes the popular and innovative bioinformatics repositories currently available, including popular primary genetic and protein sequence databases, phylogenetic databases, structure and pathway databases, microarray databases and boutique databases. It also explores the data quality and information integration issues currently involved with managing bioinformatics databases, including data quality issues that have been observed, and efforts in the data cleaning field.

Biological data integration issues are also covered in-depth, and the book demonstrates how data integration can create new repositories to address the needs of the biological communities. It also presents typical data integration architectures employed in current bioinformatics databases.

The latter part of the book covers biological data mining and biological data processing approaches using cloud-based technologies. General data mining approaches are discussed, as well as specific data mining methodologies that have been successfully deployed in biological data mining applications. Two biological data mining case studies are also included to illustrate how data, query, and analysis methods are integrated into user-friendly systems.

Aimed at researchers and developers of bioinformatics database systems, the book is also useful as a supplementary textbook for a one-semester upper-level undergraduate course, or an introductory graduate bioinformatics course.

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Bioinformatics Database Systems
Modern biological databases comprise not only data, but also sophisticated query facilities and bioinformatics data analysis tools. This book provides an exploration through the world of Bioinformatics Database Systems.

The book summarizes the popular and innovative bioinformatics repositories currently available, including popular primary genetic and protein sequence databases, phylogenetic databases, structure and pathway databases, microarray databases and boutique databases. It also explores the data quality and information integration issues currently involved with managing bioinformatics databases, including data quality issues that have been observed, and efforts in the data cleaning field.

Biological data integration issues are also covered in-depth, and the book demonstrates how data integration can create new repositories to address the needs of the biological communities. It also presents typical data integration architectures employed in current bioinformatics databases.

The latter part of the book covers biological data mining and biological data processing approaches using cloud-based technologies. General data mining approaches are discussed, as well as specific data mining methodologies that have been successfully deployed in biological data mining applications. Two biological data mining case studies are also included to illustrate how data, query, and analysis methods are integrated into user-friendly systems.

Aimed at researchers and developers of bioinformatics database systems, the book is also useful as a supplementary textbook for a one-semester upper-level undergraduate course, or an introductory graduate bioinformatics course.

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Bioinformatics Database Systems

Bioinformatics Database Systems

Bioinformatics Database Systems

Bioinformatics Database Systems

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Overview

Modern biological databases comprise not only data, but also sophisticated query facilities and bioinformatics data analysis tools. This book provides an exploration through the world of Bioinformatics Database Systems.

The book summarizes the popular and innovative bioinformatics repositories currently available, including popular primary genetic and protein sequence databases, phylogenetic databases, structure and pathway databases, microarray databases and boutique databases. It also explores the data quality and information integration issues currently involved with managing bioinformatics databases, including data quality issues that have been observed, and efforts in the data cleaning field.

Biological data integration issues are also covered in-depth, and the book demonstrates how data integration can create new repositories to address the needs of the biological communities. It also presents typical data integration architectures employed in current bioinformatics databases.

The latter part of the book covers biological data mining and biological data processing approaches using cloud-based technologies. General data mining approaches are discussed, as well as specific data mining methodologies that have been successfully deployed in biological data mining applications. Two biological data mining case studies are also included to illustrate how data, query, and analysis methods are integrated into user-friendly systems.

Aimed at researchers and developers of bioinformatics database systems, the book is also useful as a supplementary textbook for a one-semester upper-level undergraduate course, or an introductory graduate bioinformatics course.


Product Details

ISBN-13: 9780367574062
Publisher: CRC Press
Publication date: 06/30/2020
Pages: 290
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Kevin Byron is a PhD candidate in the Department of Computer Science at the New Jersey Institute of Technology.

Katherine G. Herbert is Associate Professor of Computer Science at Montclair State University.

Jason T.L. Wang is Professor of Bioinformatics and Computer Science at the New Jersey Institute of Technology.

Table of Contents

List of Figures vii

List of Tables xi

Preface xv

Acknowledgments xix

Chapter 1 Overview of Bioinformatics Databases 1

1.1 Introduction 1

1.2 Sequence Databases 2

1.3 Phylogenetic Databases 7

1.4 Structure and Pathway Databases 12

1.5 Microarray and Boutique Databases 14

Chapter 2 Biological Data Cleaning 17

2.1 Introduction 17

2.2 General Data Cleaning 24

2.3 Case Study in Biological Data Cleaning 34

Chapter 3 Biological Data Integration 47

3.1 Introduction 47

3.2 General Data Integration 48

3.3 Topics in Biological Data Integration 50

Chapter 4 Biological Data Searching 57

4.1 Introduction 57

4.2 Biological Data Searching Using Blast 58

4.3 Biological Data Searching Using the UCSC Genome Browser 58

4.4 Case Study in Phylogenetic Tree Database Search 59

4.5 Case Study in RNA Pseudoknot Database Search 73

Chapter 5 Biological Data Mining 91

5.1 Introduction 91

5.2 General Data Mining 93

5.3 Biological Data Mining 94

5.4 Case Study in Biological Motif Discovery 101

5.5 Case Study in Biological Data Mining 111

Chapter 6 Biological Network Inference 133

6.1 Introduction 133

6.2 Gene Regulatory Network Inference 135

Chapter 7 Cloud-Based Biological Data Processing 193

7.1 Introduction 193

7.2 Data Processing in the Cloud 198

Bibliography 217

Index 261

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