Introduction To Data Mining For The Life Sciences
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Introduction To Data Mining For The Life Sciences
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Author :
language : en
Publisher:
Release Date : 2012-01-01
Introduction To Data Mining For The Life Sciences written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-01-01 with categories.
Introduction To Data Mining For The Life Sciences
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Author : Rob Sullivan
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-01-07
Introduction To Data Mining For The Life Sciences written by Rob Sullivan 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 2012-01-07 with Science categories.
Data mining provides a set of new techniques to integrate, synthesize, and analyze tdata, uncovering the hidden patterns that exist within. Traditionally, techniques such as kernel learning methods, pattern recognition, and data mining, have been the domain of researchers in areas such as artificial intelligence, but leveraging these tools, techniques, and concepts against your data asset to identify problems early, understand interactions that exist and highlight previously unrealized relationships through the combination of these different disciplines can provide significant value for the investigator and her organization.
Data Mining Techniques For The Life Sciences
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Author : Oliviero Carugo
language : en
Publisher: Humana
Release Date : 2022-05-05
Data Mining Techniques For The Life Sciences written by Oliviero Carugo and has been published by Humana this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-05 with Science categories.
This third edition details new and updated methods and protocols on important databases and data mining tools. Chapters guides readers through archives of macromolecular sequences and three-dimensional structures, databases of protein-protein interactions, methods for prediction conformational disorder, mutant thermodynamic stability, aggregation, and drug response. Quality of structural data and their release, soft mechanics applications in biology, and protein flexibility are considered, too, together with pan-genome analyses, rational drug combination screening and Omics Deep Mining. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials, includes step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Data Mining Techniques for the Life Sciences, Third Edition aims to be a practical guide to researches to help further their study in this field.
Computational Life Sciences
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Author : Jens Dörpinghaus
language : en
Publisher: Springer Nature
Release Date : 2023-03-04
Computational Life Sciences written by Jens Dörpinghaus and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-04 with Computers categories.
This book broadly covers the given spectrum of disciplines in Computational Life Sciences, transforming it into a strong helping hand for teachers, students, practitioners and researchers. In Life Sciences, problem-solving and data analysis often depend on biological expertise combined with technical skills in order to generate, manage and efficiently analyse big data. These technical skills can easily be enhanced by good theoretical foundations, developed from well-chosen practical examples and inspiring new strategies. This is the innovative approach of Computational Life Sciences-Data Engineering and Data Mining for Life Sciences: We present basic concepts, advanced topics and emerging technologies, introduce algorithm design and programming principles, address data mining and knowledge discovery as well as applications arising from real projects. Chapters are largely independent and often flanked by illustrative examples and practical advise.
Life Science Data Mining
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Author : Chung-sheng Li
language : en
Publisher: World Scientific
Release Date : 2006-12-29
Life Science Data Mining written by Chung-sheng 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 2006-12-29 with Science categories.
This timely book identifies and highlights the latest data mining paradigms to analyze, combine, integrate, model and simulate vast amounts of heterogeneous multi-modal, multi-scale data for emerging real-world applications in life science.The cutting-edge topics presented include bio-surveillance, disease outbreak detection, high throughput bioimaging, drug screening, predictive toxicology, biosensors, and the integration of macro-scale bio-surveillance and environmental data with micro-scale biological data for personalized medicine. This collection of works from leading researchers in the field offers readers an exceptional start in these areas.
Biological Knowledge Discovery Handbook
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Author : Mourad Elloumi
language : en
Publisher: John Wiley & Sons
Release Date : 2015-02-04
Biological Knowledge Discovery Handbook written by Mourad Elloumi 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 2015-02-04 with Computers categories.
The first comprehensive overview of preprocessing, mining,and postprocessing of biological data Molecular biology is undergoing exponential growth in both thevolume and complexity of biological data—and knowledgediscovery offers the capacity to automate complex search and dataanalysis tasks. This book presents a vast overview of the mostrecent developments on techniques and approaches in the field ofbiological knowledge discovery and data mining (KDD)—providingin-depth fundamental and technical field information on the mostimportant topics encountered. Written by top experts, Biological Knowledge DiscoveryHandbook: Preprocessing, Mining, and Postprocessing of BiologicalData covers the three main phases of knowledge discovery (datapreprocessing, data processing—also known as datamining—and data postprocessing) and analyzes both verificationsystems 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 DataMining Combining sound theory with practical applications in molecularbiology, Biological Knowledge Discovery Handbook is idealfor courses in bioinformatics and biological KDD as well as forpractitioners and professional researchers in computer science,life science, and mathematics.
Cluster Analysis And Data Mining
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Author : Ronald S. King
language : en
Publisher: Mercury Learning and Information
Release Date : 2015-05-12
Cluster Analysis And Data Mining written by Ronald S. King and has been published by Mercury Learning and Information this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05-12 with Computers categories.
Cluster analysis is used in data mining and is a common technique for statistical data analysis used in many fields of study, such as the medical & life sciences, behavioral & social sciences, engineering, and in computer science. Designed for training industry professionals or for a course on clustering and classification, it can also be used as a companion text for applied statistics. No previous experience in clustering or data mining is assumed. Informal algorithms for clustering data and interpreting results are emphasized. In order to evaluate the results of clustering and to explore data, graphical methods and data structures are used for representing data. Throughout the text, examples and references are provided, in order to enable the material to be comprehensible for a diverse audience. A companion disc includes numerous appendices with programs, data, charts, solutions, etc. eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at [email protected]. FEATURES *Places emphasis on illustrating the underlying logic in making decisions during the cluster analysis *Discusses the related applications of statistic, e.g., Ward’s method (ANOVA), JAN (regression analysis & correlational analysis), cluster validation (hypothesis testing, goodness-of-fit, Monte Carlo simulation, etc.) *Contains separate chapters on JAN and the clustering of categorical data *Includes a companion disc with solutions to exercises, programs, data sets, charts, etc.
Data Mining For The Social Sciences
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Author : Paul Attewell
language : en
Publisher: Univ of California Press
Release Date : 2015-05
Data Mining For The Social Sciences written by Paul Attewell and has been published by Univ of California Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-05 with Political Science categories.
"We live, today, in world of big data. The amount of information collected on human behavior every day is staggering, and exponentially greater than at any time in the past. At the same time, we are inundated by stories of powerful algorithms capable of churning through this sea of data and uncovering patterns. These techniques go by many names - data mining, predictive analytics, machine learning - and they are being used by governments as they spy on citizens and by huge corporations are they fine-tunetheir advertising strategies. And yet social scientists continue mainly to employ a set of analytical tools developed in an earlier era when data was sparse and difficult to come by. In this timely book, Paul Attewell and David Monaghan provide a simple and accessible introduction to Data Mining geared towards social scientists. They discuss how the data mining approach differs substantially, and in some ways radically, from that of conventional statistical modeling familiar to most social scientists. They demystify data mining, describing the diverse set of techniques that the term covers and discussing the strengths and weaknesses of the various approaches. Finally they give practical demonstrations of how to carry out analyses using data mining tools in a number of statistical software packages. It is the hope of the authors that this book will empower social scientists to consider incorporating data mining methodologies in their analytical toolkits"--Provided by publisher.
Discovering Knowledge In Data
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Author : Daniel T. Larose
language : en
Publisher: John Wiley & Sons
Release Date : 2014-07-08
Discovering Knowledge In Data written by Daniel T. Larose 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 2014-07-08 with Computers categories.
The field of data mining lies at the confluence of predictive analytics, statistical analysis, and business intelligence. Due to the ever-increasing complexity and size of data sets and the wide range of applications in computer science, business, and health care, the process of discovering knowledge in data is more relevant than ever before. This book provides the tools needed to thrive in today’s big data world. The author demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”. By adding chapters on data modelling preparation, imputation of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining. The second edition of a highly praised, successful reference on data mining, with thorough coverage of big data applications, predictive analytics, and statistical analysis. Includes new chapters on Multivariate Statistics, Preparing to Model the Data, and Imputation of Missing Data, and an Appendix on Data Summarization and Visualization Offers extensive coverage of the R statistical programming language Contains 280 end-of-chapter exercises Includes a companion website for university instructors who adopt the book
Data Mining For Bioinformatics Applications
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Author : He Zengyou
language : en
Publisher: Woodhead Publishing
Release Date : 2015-06-09
Data Mining For Bioinformatics Applications written by He Zengyou and has been published by Woodhead Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-09 with Computers categories.
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