Computational Knowledge Discovery For Bioinformatics Research
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Computational Knowledge Discovery For Bioinformatics Research
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Author : Li, Xiao-Li
language : en
Publisher: IGI Global
Release Date : 2012-06-30
Computational Knowledge Discovery For Bioinformatics Research written by Li, Xiao-Li and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-06-30 with Medical categories.
"This book discusses the most significant research and latest practices in computational knowledge discovery approaches to bioinformatics in a cross-disciplinary manner that is useful for researchers, practitioners, academicians, mathematicians, statisticians, and computer scientists involved in the many facets of bioinformatics"--
Bioinformatics And Computational Biology In Drug Discovery And Development
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Author : William T. Loging
language : en
Publisher: Cambridge University Press
Release Date : 2016-03-17
Bioinformatics And Computational Biology In Drug Discovery And Development written by William T. Loging and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-17 with Computers categories.
A comprehensive overview of the use of computational biology approaches in the drug discovery and development process.
Knowledge Discovery In Bioinformatics
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Author : Xiaohua Hu
language : en
Publisher: John Wiley & Sons
Release Date : 2007-06-11
Knowledge Discovery In Bioinformatics written by Xiaohua Hu 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 2007-06-11 with Technology & Engineering categories.
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
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Author : Jake Y. Chen
language : en
Publisher: CRC Press
Release Date : 2009-09-01
Biological Data Mining written by Jake Y. Chen and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-09-01 with Computers categories.
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
Machine Learning In Bioinformatics
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Author : Yanqing Zhang
language : en
Publisher: John Wiley & Sons
Release Date : 2009-02-23
Machine Learning In Bioinformatics written by Yanqing Zhang 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 2009-02-23 with Computers categories.
An introduction to machine learning methods and their applications to problems in bioinformatics Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. From an internationally recognized panel of prominent researchers in the field, Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics. Coverage includes: feature selection for genomic and proteomic data mining; comparing variable selection methods in gene selection and classification of microarray data; fuzzy gene mining; sequence-based prediction of residue-level properties in proteins; probabilistic methods for long-range features in biosequences; and much more. Machine Learning in Bioinformatics is an indispensable resource for computer scientists, engineers, biologists, mathematicians, researchers, clinicians, physicians, and medical informaticists. It is also a valuable reference text for computer science, engineering, and biology courses at the upper undergraduate and graduate levels.
Bioinformatics Computing
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Author : Bryan P. Bergeron
language : en
Publisher: Prentice Hall Professional
Release Date : 2003
Bioinformatics Computing written by Bryan P. Bergeron and has been published by Prentice Hall Professional this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Computers categories.
Comprehensive and concise, this handbook has chapters on computing visualization, large database designs, advanced pattern matching and other key bioinformatics techniques. It is a practical guide to computing in the growing field of Bioinformatics--the study of how information is represented and transmitted in biological systems, starting at the molecular level.
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.
Immunological Bioinformatics
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Author : Ole Lund
language : en
Publisher: MIT Press
Release Date : 2005-06-17
Immunological Bioinformatics written by Ole Lund and has been published by MIT Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-06-17 with Computers categories.
Using bioinformatics methods to generate a systems-level view of the immune system; description of the main biological concepts and the new data-driven algorithms. Despite the fact that advanced bioinformatics methodologies have not been used as extensively in immunology as in other subdisciplines within biology, research in immunological bioinformatics has already developed models of components of the immune system that can be combined and that may help develop therapies, vaccines, and diagnostic tools for such diseases as AIDS, malaria, and cancer. In a broader perspective, specialized bioinformatics methods in immunology make possible for the first time a systems-level understanding of the immune system. The traditional approaches to immunology are reductionist, avoiding complexity but providing detailed knowledge of a single event, cell, or molecular entity. Today, a variety of experimental bioinformatics techniques connected to the sequencing of the human genome provides a sound scientific basis for a comprehensive description of the complex immunological processes. This book offers a description of bioinformatics techniques as they are applied to immunology, including a succinct account of the main biological concepts for students and researchers with backgrounds in mathematics, statistics, and computer science as well as explanations of the new data-driven algorithms in the context of biological data that will be useful for immunologists, biologists, and biochemists working on vaccine design. In each chapter the authors show interesting biological insights gained from the bioinformatics approach. The book concludes by explaining how all the methods presented in the book can be integrated to identify immunogenic regions in microorganisms and host genomes.
Biological Data Mining And Its Applications In Healthcare
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Author : Xiaoli Li
language : en
Publisher: World Scientific
Release Date : 2013-11-28
Biological Data Mining And Its Applications In Healthcare written by Xiaoli Li and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-11-28 with Science categories.
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.