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

thu@cis.drexel.edu

http://www.cis.drexel.edu/faculty/thu

 

2Dept. of Computer Science,

34 Peachtree St., Suite 1450

George State University, Atlanta, GA 30302, USA

pan@cs.gsu.edu

http://www.cs.gsu.edu/pan

 

 

 

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.