[PDF] Pattern Discovery In Biological Data Sets eBook

Author : Stanislav Plamenov Angelov
Publisher :
Page : 236 pages
File Size : 50,20 MB
Release : 2007
Category :
ISBN : 9781109985016

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There are two main approaches for extracting knowledge from sequence data. One approach compares newly acquired data with possibly, already annotated data under the assumption that data similarity implies functional similarity. The second approach mines the data for frequently occurring or surprising patterns. Such patterns are unlikely to occur at random and pinpoint candidates for further laboratory investigations.

Author : Zheng Rong Yang
Publisher : World Scientific
Page : 462 pages
File Size : 39,28 MB
Release : 2021-09-17
Category : Science
ISBN : 9811240132

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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.

Author : Fabio Fassetti
Publisher : Springer
Page : 51 pages
File Size : 37,91 MB
Release : 2017-09-01
Category : Computers
ISBN : 3319634771

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This work provides a review of biological networks as a model for analysis, presenting and discussing a number of illuminating analyses. Biological networks are an effective model for providing insights about biological mechanisms. Networks with different characteristics are employed for representing different scenarios. This powerful model allows analysts to perform many kinds of analyses which can be mined to provide interesting information about underlying biological behaviors. The text also covers techniques for discovering exceptional patterns, such as a pattern accounting for local similarities and also collaborative effects involving interactions between multiple actors (for example genes). Among these exceptional patterns, of particular interest are discriminative patterns, namely those which are able to discriminate between two input populations (for example healthy/unhealthy samples). In addition, the work includes a discussion on the most recent proposal on discovering discriminative patterns, in which there is a labeled network for each sample, resulting in a database of networks representing a sample set. This enables the analyst to achieve a much finer analysis than with traditional techniques, which are only able to consider an aggregated network of each population.

Author : Jason T. L. Wang
Publisher : Oxford University Press
Page : 272 pages
File Size : 15,90 MB
Release : 1999-10-28
Category : Science
ISBN : 0198028067

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Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.

Author : Mourad Elloumi
Publisher : John Wiley & Sons
Page : 1126 pages
File Size : 24,15 MB
Release : 2015-02-04
Category : Computers
ISBN : 1118853725

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The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD) providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing also known as data mining and data postprocessing) and analyzes both verification systems and discovery systems. BIOLOGICAL DATA PREPROCESSING Part A: Biological Data Management Part B: Biological Data Modeling Part C: Biological Feature Extraction Part D Biological Feature Selection BIOLOGICAL DATA MINING Part E: Regression Analysis of Biological Data Part F Biological Data Clustering Part G: Biological Data Classification Part H: Association Rules Learning from Biological Data Part I: Text Mining and Application to Biological Data Part J: High-Performance Computing for Biological Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.

Author : Pradipta Maji
Publisher : Springer Science & Business Media
Page : 316 pages
File Size : 39,5 MB
Release : 2014-03-19
Category : Computers
ISBN : 3319056301

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This book addresses the need for a unified framework describing how soft computing and machine learning techniques can be judiciously formulated and used in building efficient pattern recognition models. The text reviews both established and cutting-edge research, providing a careful balance of theory, algorithms, and applications, with a particular emphasis given to applications in computational biology and bioinformatics. Features: integrates different soft computing and machine learning methodologies with pattern recognition tasks; discusses in detail the integration of different techniques for handling uncertainties in decision-making and efficiently mining large biological datasets; presents a particular emphasis on real-life applications, such as microarray expression datasets and magnetic resonance images; includes numerous examples and experimental results to support the theoretical concepts described; concludes each chapter with directions for future research and a comprehensive bibliography.

Author : Laura Elnitski
Publisher : World Scientific
Page : 236 pages
File Size : 39,57 MB
Release : 2011-01-19
Category : Science
ISBN : 9814462640

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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.

Author : Ujjwal Maulik
Publisher : John Wiley & Sons
Page : 552 pages
File Size : 22,24 MB
Release : 2011-03-21
Category : Medical
ISBN : 1118097807

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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.

Author : Hemalatha Marimuthu
Publisher : LAP Lambert Academic Publishing
Page : 208 pages
File Size : 46,75 MB
Release : 2012-02
Category :
ISBN : 9783848406180

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Our ability to generate and collect data has been increasing rapidly. Improving data delivery is the top priority in bio computing today this comprehensive cutting edge guide can help by showing you how to effectively integrate bioinformatics and other powerful data mining technologies. This compact book explores the concept of data mining and discusses various data mining techniques and their applications towards bioinformatics. It is primarily designed for budding researchers in computer science. You will learn how to Use data mining to establish competitive advantage, Solve biological problems faster by exploiting clustering and pattern discovery, Evaluate various data mining solutions to the high dimensional datasets, Leverage your data mining utility via the internet, other biological resources i.e, medical datasets, bioinformatics datasets etc, In addition to provide a detailed overview and strategic analysis of the available data mining technologies, the book serves as a practical guide to design and deployment of algorithms and how to interpret, evaluate and discuss according to our research for the research scholars.

Author : Jason T. L. Wang
Publisher : Oxford University Press
Page : 280 pages
File Size : 29,13 MB
Release : 1999-10-28
Category : Science
ISBN : 9780198028062

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Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.