Welcome to the Bioinformatics Center, devoted to the areas of bioinformatics and life science informatics. We are proud of our strong research and educational connections to departments and schools at University Heights, Newark, including the Bioinformatics Program, the Information Technology Program, the Division of Biological Sciences, and the College of Computing Sciences at NJIT; the Public Health Research Institute; the University of Medicine and Dentistry of New Jersey; and Rutgers University.

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Modern biological databases comprise not only data, but also sophisticated query facilities and bioinformatics data analysis tools. This book provides an exploration through the world of Bioinformatics Database Systems. The book summarizes the popular and innovative bioinformatics repositories currently available, including popular primary genetic and protein sequence databases, phylogenetic databases, structure and pathway databases, microarray databases and boutique databases. It also explores the data quality and information integration issues currently involved with managing bioinformatics databases, including data quality issues that have been observed, and efforts in the data cleaning field.

This book surveys the developments of techniques and approaches on pattern recognition related to Computational Molecular Biology. Providing a broad coverage of the field, the authors cover fundamental and technical information on these techniques and approaches, as well as discussing their related problems. The text consists of twenty nine chapters, organized into seven parts: Pattern Recognition in Sequences, Pattern Recognition in Secondary Structures, Pattern Recognition in Tertiary Structures, Pattern Recognition in Quaternary Structures, Pattern Recognition in Microarrays, Pattern Recognition in Phylogenetic Trees, and Pattern Recognition in Biological Networks.

Biologists are stepping up their efforts in understanding the biological processes that underlie disease pathways in the clinical contexts. This has resulted in a flood of biological and clinical data from genomic and protein sequences, DNA microarrays, protein interactions, biomedical images, to disease pathways and electronic health records. To exploit these data for discovering new knowledge that can be translated into clinical applications, there are fundamental data analysis difficulties that have to be overcome. Practical issues such as handling noisy and incomplete data, processing compute-intensive tasks, and integrating various data sources, are new challenges faced by biologists in the post-genome era. This book covers the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrates with real applications how biologists and clinical scientists can employ data mining to enable them to make meaningful observations and discoveries from a wide array of heterogeneous data from molecular biology to pharmaceutical and clinical domains.

Bioinformatics, a field devoted to the interpretation and analysis of biological data using computational techniques, has evolved tremendously in recent years due to the explosive growth of biological information generated by the scientific community. Soft computing is a consortium of methodologies that work synergistically and provides, in one form or another, flexible information processing capabilities for handling real-life ambiguous situations. Several research articles dealing with the application of soft computing tools to bioinformatics have been published in the recent past; however, they are scattered in different journals, conference proceedings and technical reports, thus causing inconvenience to readers, students and researchers. This book, unique in its nature, is aimed at providing a treatise in a unified framework, with both theoretical and experimental results, describing the basic principles of soft computing and demonstrating the various ways in which they can be used for analyzing biological data in an efficient manner. Interesting research articles from eminent scientists around the world are brought together in a systematic way such that the reader will be able to understand the issues and challenges in this domain, the existing ways of tackling them, recent trends, and future directions. This book is the first of its kind to bring together two important research areas, soft computing and bioinformatics, in order to demonstrate how the tools and techniques in the former can be used for efficiently solving several problems in the latter.

Bioinformatics is the science of managing, mining, integrating, and interpreting information from biological data at the genomic, metabalomic, proteomic, phylogenetic, cellular, or whole organism levels. The need for bioinformatics tools and expertise has increased as genome sequencing projects have resulted in an exponential growth in complete and partial sequence databases. These and other projects require the development of new ways to interpret the flood of biological data that exists today and that is anticipated in the future. Data mining or knowledge discovery from data (KDD), in its most fundamental form, is to extract interesting, nontrivial, implicit, previously unknown and potentially useful information from data. With the substantial growth of biological data, KDD will play a significant role in analyzing the data and in solving emerging problems. The aim of this book is to introduce the reader to some of the best techniques for data mining in bioinformatics (BIOKDD) in the hope that the reader will build on them to make new discoveries on his or her own.


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