Pattern Discovery in Biosequences: From Simple to Complex

Pattern Discovery in Biosequences: From Simple to Complex

Simona Este Rombo (Università della Calabria, Italy) and Luigi Palopoli (Università della Calabria, Italy)
Copyright: © 2008 |Pages: 21
DOI: 10.4018/978-1-59904-162-9.ch004
OnDemand PDF Download:


In the last years, the information stored in biological data-sets grew up exponentially, and new methods and tools have been proposed to interpret and retrieve useful information from such data. Most biological data-sets contain biological sequences (e.g., DNA and protein sequences). Thus, it is much significant to have techniques available capable of mining patterns from such sequences to discover interesting information from them. For instance, singling out for common or similar sub-sequences in sets of bio-sequences is sensible as these are usually associated to similar biological functions expressed by the corresponding macromolecules. The aim of this chapter is to explain how pattern discovery can be applied to deal with such important biological problems, describing also a number of relevant techniques proposed in the literature. A simple formalization of the problem is given and specialized for each of the presented approaches. Such a formalization should ease reading and understanding the illustrated material by providing a simple-to-follow roadmap scheme through the diverse methods for pattern extraction we are going to illustrate.

Complete Chapter List

Search this Book:
Table of Contents
Pascal Poncelet, Maguelonne Teisseire, Florent Masseglia
Chapter 1
Dan A. Simovici
This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally... Sample PDF
Metric Methods in Data Mining
Chapter 2
Osmar R. Zaïane, Mohammed El-Hajj
Frequent Itemset Mining (FIM) is a key component of many algorithms that extract patterns from transactional databases. For example, FIM can be... Sample PDF
Bi-Directional Constraint Pushing in Frequent Pattern Mining
Chapter 3
Hui Xiong, Pang-Ning Tan, Vipin Kumar, Wenjun Zhou
This chapter presents a framework for mining highly-correlated association patterns named hyperclique patterns. In this framework, an objective... Sample PDF
Mining Hyperclique Patterns: A Summary of Results
Chapter 4
Simona Este Rombo, Luigi Palopoli
In the last years, the information stored in biological data-sets grew up exponentially, and new methods and tools have been proposed to interpret... Sample PDF
Pattern Discovery in Biosequences: From Simple to Complex
Chapter 5
Gregor Leban, Minca Mramor, Blaž Zupan, Janez Demšar, Ivan Bratko
Data visualization plays a crucial role in data mining and knowledge discovery. Its use is, however, often difficult due to the large number of... Sample PDF
Finding Patterns in Class-Labeled Data Using Data Visualization
Chapter 6
Yeow Wei Choong, Anne Laurent, Dominique Laurent
In the context of multidimensional data, OLAP tools are appropriate for the navigation in the data, aiming at discovering pertinent and abstract... Sample PDF
Summarizing Data Cubes Using Blocks
Chapter 7
Yutaka Matsuo, Junichiro Mori, Mitsuru Ishizuka
This chapter describes social network mining from the Web. Since the end of the 1990s, several attempts have been made to mine social network... Sample PDF
Social Network Mining from the Web
Chapter 8
Donato Malerba, Margherita Berardi, Michelangelo Ceci
This chapter introduces a data mining method for the discovery of association rules from images of scanned paper documents. It argues that a... Sample PDF
Discovering Spatio-Textual Association Rules in Document Images
Chapter 9
Mining XML Documents  (pages 198-219)
Laurent Candillier, Ludovic Denoyer, Patrick Gallinari, Marie Christine Rousset, Alexandre Termier, Anne-Marie Vercoustre
XML documents are becoming ubiquitous because of their rich and flexible format that can be used for a variety of applications. Giving the... Sample PDF
Mining XML Documents
Chapter 10
Sascha Schulz, Myra Spiliopoulou, Rene Schult
We study the issue of discovering and tracing thematic topics in a stream of documents. This issue, often studied under the label “topic evolution”... Sample PDF
Topic and Cluster Evolution Over Noisy Document Streams
Chapter 11
Cyrille J. Joutard, Edoardo M. Airoldi, Stephen E. Edoardo M., Tanzy M. Love
Statistical models involving a latent structure often support clustering, classification, and other data-mining tasks. Parameterizations... Sample PDF
Discovery of Latent Patterns with Hierarchical Bayesian Mixed-Membership Models and the Issue of Model Choice
About the Editors
About the Contributors