Summary
The aim of this book is to introduce the reader to some of the best techniques for data mining in bioinformatics in the hope that the reader will build on them to make new discoveries on his or her own. The book contains twelve chapters in four parts, namely, overview, sequence and structure alignment, biological data mining, and biological data management. This chapter provides an introduction to the field and describes how the chapters in the book relate to one another.
Keywords
- Data Mining
- Biological Data
- Structure Alignment
- Markov Chain Monte Carlo Method
- Data Mining Method
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, access via your institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag London Limited
About this chapter
Cite this chapter
Wang, J.T.L., Zaki, M.J., Toivonen, H.T.T., Shasha, D. (2005). Introduction to Data Mining in Bioinformatics. In: Wu, X., Jain, L., Wang, J.T., Zaki, M.J., Toivonen, H.T., Shasha, D. (eds) Data Mining in Bioinformatics. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-059-1_1
Download citation
DOI: https://doi.org/10.1007/1-84628-059-1_1
Publisher Name: Springer, London
Print ISBN: 978-1-85233-671-4
Online ISBN: 978-1-84628-059-7
eBook Packages: Computer ScienceComputer Science (R0)