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Data Mining For Bioinformatics Applications

Author: He Zengyou
Publisher: Woodhead Publishing
ISBN: 008100107X
Size: 37.68 MB
Format: PDF, ePub
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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

Author: Sumeet Dua
Publisher: CRC Press
ISBN: 0849328012
Size: 21.79 MB
Format: PDF, Kindle
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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 help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field. The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections: Supplies a complete overview of the evolution of the field and its intersection with computational learning Describes the role of data mining in analyzing large biological databases—explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data Covers supervised learning using classification techniques most commonly used in bioinformatics—addressing the need for validation and benchmarking of inferences derived using either clustering or classification The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biological databases, it considers systems of both single and ensemble classifiers and shares effort-saving tips for model selection and performance estimation strategies.

Textbook Of Machine Learning And Data Mining

Author: Hiroshi Mamitsuka
ISBN: 9784991044502
Size: 64.69 MB
Format: PDF
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Data-driven approaches, particularly machine learning and data mining, are the main driving force of the current artificial intelligence technology. This book covers a wide variety of methods in machine learning and data mining, dividing them from a viewpoint of data types, which begin with rather simple vectors and end by graphs and also combination of different data types. This book describes standard techniques of machine learning and data mining for each data type, especially focusing on the relevance and difference among them. Also after explaining a series of machine learning methods for seven different data types, this book has a chapter for standard validation methods on empirical results obtained by applying machine learning methods to data. This book can be used for a variety of objectives, including an introductory textbook of studying machine learning and a (first step) book to start machine learning research, etc.

Knowledge Discovery In Bioinformatics

Author: Xiaohua Hu
Publisher: John Wiley & Sons
ISBN: 9780470124635
Size: 72.96 MB
Format: PDF, ePub, Mobi
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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.


Author: M. H. Fulekar
Publisher: Springer Science & Business Media
ISBN: 1402088809
Size: 14.44 MB
Format: PDF
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Bioinformatics, computational biology, is a relatively new field that applies computer science and information technology to biology. In recent years, the discipline of bioinformatics has allowed biologists to make full use of the advances in Computer sciences and Computational statistics for advancing the biological data. Researchers in life sciences generate, collect and need to analyze an increasing number of different types of scientific data, DNA, RNA and protein sequences, in-situ and microarray gene expression including 3D protein structures and biological pathways. This book is aiming to provide information on bioinformatics at various levels. The chapters included in this book cover introductory to advanced aspects, including applications of various documented research work and specific case studies related to bioinformatics. This book will be of immense value to readers of different backgrounds such as engineers, scientists, consultants and policy makers for industry, government, academics and social and private organisations.

Advanced Data Mining Technologies In Bioinformatics

Author: Hui-Huang Hsu
Publisher: IGI Global
ISBN: 1591408636
Size: 69.38 MB
Format: PDF, ePub
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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 For Scientific And Engineering Applications

Author: R.L. Grossman
Publisher: Springer Science & Business Media
ISBN: 9781402000331
Size: 57.70 MB
Format: PDF, ePub, Mobi
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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.

Fuzzy Systems And Knowledge Discovery

Author: Lipo Wang
Publisher: Springer Science & Business Media
ISBN: 3540459162
Size: 65.24 MB
Format: PDF, ePub, Docs
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This book constitutes the refereed proceedings of the Third International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006, held in federation with the Second International Conference on Natural Computation ICNC 2006. The book presents 115 revised full papers and 50 revised short papers. Coverage includes neural computation, quantum computation, evolutionary computation, DNA computation, fuzzy computation, granular computation, artificial life, innovative applications to knowledge discovery, finance, operations research, and more.

Data Mining

Author: Sushmita Mitra
Publisher: John Wiley & Sons
ISBN: 9780471474883
Size: 11.88 MB
Format: PDF
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First title to ever present soft computing approaches and theirapplication in data mining, along with the traditionalhard-computing approaches Addresses the principles of multimedia data compressiontechniques (for image, video, text) and their role in datamining Discusses principles and classical algorithms on stringmatching and their role in data mining

Data Mining In Bioinformatics

Author: Jason T. L. Wang
Publisher: Springer Science & Business Media
ISBN: 1846280591
Size: 38.59 MB
Format: PDF, Mobi
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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.