Pattern Discovery In Bioinformatics
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Pattern Discovery In Bioinformatics
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Author : Laxmi Parida
language : en
Publisher: CRC Press
Release Date : 2007-07-04
Pattern Discovery In Bioinformatics written by Laxmi Parida and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-04 with Computers categories.
The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systema
Biological Pattern Discovery With R Machine Learning Approaches
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Author : Zheng Rong Yang
language : en
Publisher: World Scientific
Release Date : 2021-09-17
Biological Pattern Discovery With R Machine Learning Approaches written by Zheng Rong Yang and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-17 with Science categories.
This book provides the research directions for new or junior researchers who are going to use machine learning approaches for biological pattern discovery. The book was written based on the research experience of the author's several research projects in collaboration with biologists worldwide. The chapters are organised to address individual biological pattern discovery problems. For each subject, the research methodologies and the machine learning algorithms which can be employed are introduced and compared. Importantly, each chapter was written with the aim to help the readers to transfer their knowledge in theory to practical implementation smoothly. Therefore, the R programming environment was used for each subject in the chapters. The author hopes that this book can inspire new or junior researchers' interest in biological pattern discovery using machine learning algorithms.
Advances In Genomic Sequence Analysis And Pattern Discovery
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Author : Laura Elnitski
language : en
Publisher: World Scientific
Release Date : 2011
Advances In Genomic Sequence Analysis And Pattern Discovery written by Laura Elnitski and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Science categories.
Mapping the genomic landscapes is one of the most exciting frontiers of science. We have the opportunity to reverse engineer the blueprints and the control systems of living organisms. Computational tools are key enablers in the deciphering process. This book provides an in-depth presentation of some of the important computational biology approaches to genomic sequence analysis. The first section of the book discusses methods for discovering patterns in DNA and RNA. This is followed by the second section that reflects on methods in various ways, including performance, usage and paradigms.
Pattern Discovery Using Sequence Data Mining
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Author : Pradeep Kumar
language : en
Publisher:
Release Date : 2011-07-01
Pattern Discovery Using Sequence Data Mining written by Pradeep Kumar and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-01 with Sequential pattern mining categories.
"This book provides a comprehensive view of sequence mining techniques, and present current research and case studies in Pattern Discovery in Sequential data authored by researchers and practitioners"--
Computational Intelligence And Pattern Analysis In Biology Informatics
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Author : Ujjwal Maulik
language : en
Publisher: John Wiley & Sons
Release Date : 2011-03-21
Computational Intelligence And Pattern Analysis In Biology Informatics written by Ujjwal Maulik and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-21 with Medical categories.
An invaluable tool in Bioinformatics, this unique volume provides both theoretical and experimental results, and describes basic principles of computational intelligence and pattern analysis while deepening the reader's understanding of the ways in which these principles can be used for analyzing biological data in an efficient manner. This book synthesizes current research in the integration of computational intelligence and pattern analysis techniques, either individually or in a hybridized manner. The purpose is to analyze biological data and enable extraction of more meaningful information and insight from it. Biological data for analysis include sequence data, secondary and tertiary structure data, and microarray data. These data types are complex and advanced methods are required, including the use of domain-specific knowledge for reducing search space, dealing with uncertainty, partial truth and imprecision, efficient linear and/or sub-linear scalability, incremental approaches to knowledge discovery, and increased level and intelligence of interactivity with human experts and decision makers Chapters authored by leading researchers in CI in biology informatics. Covers highly relevant topics: rational drug design; analysis of microRNAs and their involvement in human diseases. Supplementary material included: program code and relevant data sets correspond to chapters.
Emerging Trends In Applications And Infrastructures For Computational Biology Bioinformatics And Systems Biology
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Author : Hamid R Arabnia
language : en
Publisher: Morgan Kaufmann
Release Date : 2016-03-25
Emerging Trends In Applications And Infrastructures For Computational Biology Bioinformatics And Systems Biology written by Hamid R Arabnia and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-25 with Computers categories.
Emerging Trends in Applications and Infrastructures for Computational Biology, Bioinformatics, and Systems Biology: Systems and Applications covers the latest trends in the field with special emphasis on their applications. The first part covers the major areas of computational biology, development and application of data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques for the study of biological and behavioral systems. The second part covers bioinformatics, an interdisciplinary field concerned with methods for storing, retrieving, organizing, and analyzing biological data. The book also explores the software tools used to generate useful biological knowledge. The third part, on systems biology, explores how to obtain, integrate, and analyze complex datasets from multiple experimental sources using interdisciplinary tools and techniques, with the final section focusing on big data and the collection of datasets so large and complex that it becomes difficult to process using conventional database management systems or traditional data processing applications. - Explores all the latest advances in this fast-developing field from an applied perspective - Provides the only coherent and comprehensive treatment of the subject available - Covers the algorithm development, software design, and database applications that have been developed to foster research
Data Mining In Bioinformatics
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Author : Jason T. L. Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2005
Data Mining In Bioinformatics written by Jason T. L. Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005 with Computers categories.
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.
Data Mining For Bioinformatics
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Author : Sumeet Dua
language : en
Publisher: CRC Press
Release Date : 2012-11-06
Data Mining For Bioinformatics written by Sumeet Dua and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-11-06 with Computers categories.
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.