Knowledge Discovery in Bioinformatics: Techniques,
Methods and Applications
Author Information
Xiaohua
Hu1, Yi Pan2
1College of Information
Science and Technology,
Drexel
University, Philadelphia, PA 19104, USA
http://www.cis.drexel.edu/faculty/thu
2Dept. of Computer Science,
34
Peachtree St., Suite 1450
George
State University, Atlanta, GA 30302, USA
Book's subject and purpose
Bioinformatics is the science of integrating,
managing, mining, and interpreting information from biological data sets. While tremendous progress has been made
over the years, many of the fundamental problems in bioinformatics, such as
protein structure prediction or gene finding, data retrieval and integration
are still open. In recent years, high-throughput experimental methods in
molecular biology have resulted in enormous amounts of data. Mining
bioinformatics data is an emerging area of intersection between bioinformatics
and data mining. The objective of this book is to facilitate collaboration
between data mining researchers and bioinformaticians
by presenting cutting edge research topics and methodologies in the area of
data mining for bioinformatics. The book feature chapters from noted
experts in the field, and the latest data mining research in
bioinformatics. The chapters cover
topics that propose novel data mining techniques for tasks such as:
Features
This book contains articles written by
experts on a wide range of topics that are associated with novel methods,
techniques and applications of data mining in the analysis and management of
bioinformatics data sets. It contains chapters on RNA and protein structure
analysis, DNA computing, sequence mapping, genome comparison, gene expression
data mining, metabolic network modeling, phyloinformatics,
biomedical literature data mining, biological data integration and
searching. The important work of
some representative researchers in bioinformatics is brought together for the
first time in one volume. The topic is treated in depth and is related to,
where applicable, other emerging technologies such as data mining and
visualization. The goal of the book is to introduce readers to the principle
techniques of data mining in bioinformatics in the hope that they will build on
them to make new discoveries of their own.
The
critical bioinformatics research areas are protein structure prediction, gene
finding, microarray data analysis, protein-protein
interaction, molecular modeling in drug design and structural biology. The
computational areas include data integration and information retrieval of heterogenous bioinformatics data set, high performance
computing algorithms, new data mining algorithms and tools for image analysis
and molecular modeling, protein structure prediction. The multidisciplinary research will
provide superior tools for prediction and annotation of protein and gene,
detection and treatment of disease, and improved knowledge of the molecular
mechanisms producing disease and the neural mechanisms of behavior.
Intended Audience
The
major objective of this book is to stimulate new multidisciplinary research and
the development of cutting-edge data mining methods, techniques and tools to
solve problems in bioinformatics. The goal of this
book is to help readers understand state-of-the-art techniques in
bioinformatics data mining and data management. The intended audiences are bioinformatic specialists in academia and industry, e.g.,
pharmaceuticals, data mining researchers, postgraduates, molecular
biologists who rely on computers and mathematical scientists with interests in
biology.