Bio-medical Ontologies Maintenance and Change Management

Preface
The molecular biology community faces an inundation of data. This implies that
most of the contents of databanks will be recently determined data, and features,
such as quality, will be characteristic of the newest methods of measurement. New
experimental techniques will increase the amount and diversity of data; for example,
the Genomics, Proteomics and Expression Profiles projects. 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 modelling 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.
Management of scientific data is critical in supporting discoveries in the life sciences
field. Over the past several years, bioinformatics has become an allencompassing
term for everything relating to both computer science and biology.
The goal of this book is to cover biomedical data and applications identifying new
issues and directions for future research in biomedical domain. The book will
become a useful guide for researchers, practitioners, and graduate-level students interested
in learning state-of-the-art development in biomedical data management,
data-intensive bioinformatics systems, and other miscellaneous biological database
applications. The content of this book is at an introductory and medium technical
level.
There are 14 chapters presented in this book. Individual chapters have been
written by selected and accomplished research teams active in the research of respective
topics. Each chapters covers an important aspect of the fast growing topic
of bioinformatics. Complication of these book chapters on the whole addresses
this book’s topic with varying degrees of balance between biomedical data models
and their real-world applications. Chapter 1 discusses trends in biomedical data
and applications. It focuses on the areas of biomedical data integration, access,
and interoperability as these areas form the cornerstone of the field. In chapters 2
through 10, we introduce biomedical data management and general data analysis
practices essential to post-genome biology. Chapters 11 through to 14 discuss
methodologies of some of the biomedical applications.