Data Mining for Bioinformatics Applications

Data Mining for Bioinformatics Applications PDF

Author: He Zengyou

Publisher: Woodhead Publishing

Published: 2015-06-09

Total Pages: 100

ISBN-13: 008100107X

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Data Mining for Bioinformatics Applications provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems, including problem definition, data collection, data preprocessing, modeling, and validation. The text uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems, containing 45 bioinformatics problems that have been investigated in recent research. For each example, the entire data mining process is described, ranging from data preprocessing to modeling and result validation. Provides valuable information on the data mining methods have been widely used for solving real bioinformatics problems Uses an example-based method to illustrate how to apply data mining techniques to solve real bioinformatics problems Contains 45 bioinformatics problems that have been investigated in recent research

Data Mining for Bioinformatics

Data Mining for Bioinformatics PDF

Author: Sumeet Dua

Publisher: CRC Press

Published: 2012-11-06

Total Pages: 351

ISBN-13: 1466588667

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Covering theory, algorithms, and methodologies, as well as data mining technologies, Data Mining for Bioinformatics provides a comprehensive discussion of data-intensive computations used in data mining with applications in bioinformatics. It supplies a broad, yet in-depth, overview of the application domains of data mining for bioinformatics to he

Advanced Data Mining Technologies in Bioinformatics

Advanced Data Mining Technologies in Bioinformatics PDF

Author: Hui-Huang Hsu

Publisher: IGI Global

Published: 2006-01-01

Total Pages: 343

ISBN-13: 1591408636

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"This book covers research topics of data mining on bioinformatics presenting the basics and problems of bioinformatics and applications of data mining technologies pertaining to the field"--Provided by publisher.

Data Mining in Bioinformatics

Data Mining in Bioinformatics PDF

Author: Jason T. L. Wang

Publisher: Springer Science & Business Media

Published: 2006-03-30

Total Pages: 340

ISBN-13: 1846280591

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Written especially for computer scientists, all necessary biology is explained. Presents new techniques on gene expression data mining, gene mapping for disease detection, and phylogenetic knowledge discovery.

Biological Data Mining

Biological Data Mining PDF

Author: Jake Y. Chen

Publisher: CRC Press

Published: 2009-09-01

Total Pages: 736

ISBN-13: 1420086855

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Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplin

Computation in BioInformatics

Computation in BioInformatics PDF

Author: S. Balamurugan

Publisher: John Wiley & Sons

Published: 2021-10-19

Total Pages: 354

ISBN-13: 1119654718

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COMPUTATION IN BIOINFORMATICS Bioinformatics is a platform between the biology and information technology and this book provides readers with an understanding of the use of bioinformatics tools in new drug design. The discovery of new solutions to pandemics is facilitated through the use of promising bioinformatics techniques and integrated approaches. This book covers a broad spectrum of the bioinformatics field, starting with the basic principles, concepts, and application areas. Also covered is the role of bioinformatics in drug design and discovery, including aspects of molecular modeling. Some of the chapters provide detailed information on bioinformatics related topics, such as silicon design, protein modeling, DNA microarray analysis, DNA-RNA barcoding, and gene sequencing, all of which are currently needed in the industry. Also included are specialized topics, such as bioinformatics in cancer detection, genomics, and proteomics. Moreover, a few chapters explain highly advanced topics, like machine learning and covalent approaches to drug design and discovery, all of which are significant in pharma and biotech research and development. Audience Researchers and engineers in computation biology, information technology, bioinformatics, drug design, biotechnology, pharmaceutical sciences.

Data Mining for Scientific and Engineering Applications

Data Mining for Scientific and Engineering Applications PDF

Author: R.L. Grossman

Publisher: Springer Science & Business Media

Published: 2013-12-01

Total Pages: 608

ISBN-13: 1461517338

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Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Data Mining

Data Mining PDF

Author: Sushmita Mitra

Publisher: John Wiley & Sons

Published: 2005-01-21

Total Pages: 423

ISBN-13: 0471474886

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First title to ever present soft computing approaches and their application in data mining, along with the traditional hard-computing approaches Addresses the principles of multimedia data compression techniques (for image, video, text) and their role in data mining Discusses principles and classical algorithms on string matching and their role in data mining

Knowledge Discovery in Bioinformatics

Knowledge Discovery in Bioinformatics PDF

Author: Xiaohua Hu

Publisher: John Wiley & Sons

Published: 2007-06-11

Total Pages: 400

ISBN-13: 9780470124635

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The purpose of this edited book is to bring together the ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edge research topics such as, gene expressions, protein/RNA structure prediction, phylogenetics, sequence and structural motifs, genomics and proteomics, gene findings, drug design, RNAi and microRNA analysis, text mining in bioinformatics, modelling of biochemical pathways, biomedical ontologies, system biology and pathways, and biological database management.

Biological Data Mining And Its Applications In Healthcare

Biological Data Mining And Its Applications In Healthcare PDF

Author: Xiaoli Li

Publisher: World Scientific

Published: 2013-11-28

Total Pages: 437

ISBN-13: 9814551023

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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 will cover the fundamentals of state-of-the-art data mining techniques which have been designed to handle such challenging data analysis problems, and demonstrate 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.