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Book "Biological Database Modeling"
  Audience
  Introduction
  Editors
  Preface
  Topics
  Publisher

You can purchase the book at Amazon.com

Target Audience

The intended target audience of this book are readers who wish to learn about the current research topics and trends in biological database technologies. Specifically, the book will provide a theoretical perspective and practical solutions to graduate students , researchers and practitioners working in the areas of advanced database systems, biological data management, and biological information systems.

Introduction

Database management systems are designed to support large volumes of data storage, data processing, data querying, and most recently, data mining and knowledge discovery activities. Rapid increase in computing power and advances in data management techniques in recent decades have led many researchers to pursue knowledge discovery with databases and database management systems as their primary computing platform. A recent trend of general database research in this direction has been the incorporation of domain semantics into the representation and management of data.

Compared with data from general application domains, modern biological data has many unique characteristics. Biological data are often characterized as having large volumes, complex structures, high dimensionality, evolving biological concepts, and insufficient data modeling practices. These characteristics require database researchers and developers to make many special considerations while developing biological databases and database systems. They also have made biological data management and knowledge discovery in databases challenging.

Database modeling in the biological domain has received increasing attention both as a research topic and as a practice in biological computings. By carefully representing the structure, semantics, and querying requirements of large volumes of biological data, researchers and developers can help biologists track, query, analyze, and data-mine data from high-throughput genomics, gene expression profiling, proteomics, metabolomics, genotyping, text ming, and chemical screening projects.

In this book, we invite papers that cover the fast-growing topic of biological database modeling with an emphasis on both computational techniques and real-world applications. The book will become a useful guide for researchers, practitioners, and graduate-level students interested in learning state-of-the-art development in biological/biomedical data management, data-intensive bioinformatics systems, and other miscellaneous biological database applications.

Editors

Preface Excerpt

[an excerpt] "Compared with data from general business application domains, Omics data have many unique characteristics that make them challenging to manage. The following are some of the highlights:

1. Omics data tend to have more complex and evolving data structures than business data. Biological data representation often depends on application scenarios. For example, biological sequences such as DNA and proteins can be either represented as simple character strings or connected nodes in three-dimensional spatial vectors.
2. Omics data are more likely to come from distributed heterogeneous locations worldwide than business data. To study systems biology, a bioinformatics researcher may routinely download genome data from the Genome database center at the University of California Santa Cruz, collect literature abstracts from the PubMed database at the National Library of Medicine in Maryland, collect proteome information from the Swissprot database in Switzerland, and collect pathway data from the KEGG database in Japan.
3. Omics data tend to reflect the general nature of scientific experimental data: high-volume, noisy, formatted differently, incomplete, and incompatible. In contrast, data collected from business transactions tend to contain far less errors, are often more accurate, and show more consistencies in data formats/coverage.
4. Omics data also lag behind business data in ontology development. For example, Gene Ontology (GO) as a standard to control vocabularies for genes was not around until a decade ago, whereas standards such as industrial product categories have been around for ages."

Topics Suggested in Call-for-papers (submission closed)

Specific topics of interest of this book include, but are not limited to

A. Conceptual Models of Emerging Large-scale Biological Data

• Conceptual/semantic biological data modeling principles and common practices
• Current biological data modeling challenges
• Modeling Data Uncertainty and Imprecision in Biology
• Modeling large-scale motifs, genotypes, pathways, molecular networks, and protein complexes.
• Modeling data from high-throughput gene expression, proteomics, and chemical screening pipelines
• Evolution and integration of the biological data models
• Biological standard data exchange formats, XML biological data models
• Gene ontology, unified medical language systems
• Conceptual design of biological databases using biological data models
• Software systems supporting biological data modeling

B. Data and Query Processing Models in Biological Database Systems

• Algebraic Operations of the Biological Data
• Biological Data Operators and web services
• Special biological data Indexing techniques
• Query Processing and Query Optimization Techniques for Manipulating Biological Data
• Integration of Biological Data and Queries
• Interoperation Among Multiple Biological Database Queries
• Biological workflow modeling and data processing systems
• Dissemination of biological data and query results

C. Model-based Biological Knowledge Discovery Systems

• System-scale biological discovery challenges and opportunities
• Model-driven Knowledge Discovery Software Systems for Biology
• General Data-rich Biological Information System Development
• Integrated Systems for Comparative Genomics, Proteomics, Functional Genomics, Pharmacogenomics, and Systomics studies.

Publisher

ARTECH HOUSE (BOSTON) 685 Canton Street, Norwood, MA 02062 , USA