Data Mining Techniques for the Life Sciences, Carugo Oliviero, Eisenhaber Frank
Автор: Aegerter Christof M. Название: Introductory Physics for Biological Scientists ISBN: 1108466508 ISBN-13(EAN): 9781108466509 Издательство: Cambridge Academ Рейтинг: Цена: 9979.00 р. Наличие на складе: Поставка под заказ.
Описание: An introduction to the fundamental physical principles related to the study of biological phenomena. Chapters are structured around biological examples, and the topics covered include waves, optics and mechanics. With quiz questions and a detailed appendix, it is perfect for students looking to develop their quantitative and analytical tools.
Описание: Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition presents an applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies.
Автор: Oliviero Carugo; Frank Eisenhaber Название: Data Mining Techniques for the Life Sciences ISBN: 1493935704 ISBN-13(EAN): 9781493935703 Издательство: Springer Рейтинг: Цена: 25155.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Part I: Data Basses
1. Update on Genomic Databases and Resources at the National Center for Biotechnology Information
Tatiana Tatusova
2. Protein Structure Databases
Roman A. Laskowski
3. The MIntAct Project and Molecular Interaction Databases
Luana Licata and Sandra Orchard
4. Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants
M. Michael Gromiha, P. Anoosha, and Liang-Tsung Huang
5. Classification and Exploration of 3D Protein Domain Interactions using Kbdock
Anisah W. Ghoorah, Marie-Dominique Devignes, Malika Smaпl-Tabbone, David W. Ritchie
6. Data Mining of Macromolecular Structures
Bart van Beusekom, Anastassis Perrakis, and Robbie P. Joosten
7. Criteria to Extract High Quality Protein Data Bank Subsets for Structure Users
Oliviero Carugo and Kristina Djinovic-Carugo
8. Homology-based Annotation of Large Protein Datasets
Marco Punta and Jaina Mistry
PART II: Computational Techniques
9. Identification and Correction Of Erroneous Protein Sequences in Public Databases
Lбszlу Patthy10. Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps Of Protein Assemblies Using Evolutionary Information From Aligned Homologous Proteins Ramachandran Rakesh and Narayanaswamy Srinivasan
11. Systematic Exploration of an Efficient Amino Acid Substitution Matrix, MIQS
Kentaro Tomii and Kazunori Yamada
12. Promises and Pitfalls of High Throughput Biological Assays
Greg Finak and Raphael Gottardo
13. Optimizing RNA-seq Mapping with STAR
Alexander Dobin and Thomas R. Gingeras
PART III: Prediction Methods
14. Predicting Conformational Disorder
Philippe Lieutaud, Franзois Ferron, and Sonia Longhi
15. Classification of Protein Kinases Influenced By Conservation of Substrate Binding Residues
16. Spectral-Statistical Approach for Revealing Latent Regular Structures in DNA Sequence
Maria Chaley and Vladimir Kutyrkin
17.Protein Crystallizability
Pawel Smialowski and Philip Wong
18. Analysis and Visualization of ChIP-Seq and RNA-Seq Sequence Alignments using ngs.plot
Yong-Hwee Eddie Loh, and Li Shen
19. Dataming with ontologies
Robert Hoehndorft, Georgios V. Gkoutos, and Paul N. Schofield
20. Functional Analysis of Metabolomics Data
Mуnica Chagoyen, Javier Lуpez-Ibбсez, and Florencio Pazos
21. Bacterial Genomics Data Analysis in the Next-Generation Sequencing Era
Massimiliano Orsini, Gianmauro Cuccuru, Paolo Uva, and Giorgio Fotia
22. A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-Synonymous Variants
Stefano Castellana, Caterina Fusilli, and Tommaso Mazza
23. Recommendation Techniques for Drug-Target Interaction Prediction and Drug-Repositioning
Salvatore Alaimo, Rosalba Giugno, and Alfredo Pulvirenti 24. Protein Residue Contacts and Prediction Methods
Badri Adhikari and Jianlin Cheng
25. The Recipe for Protein Sequence-Based Function Prediction and its Implementation in the Annotator Software Environment
Birgit Eisenhaber, Durga Kuchibhatla, Westley Sherman, Fernanda L. Sirota, Igor N. Berezovsky, Wing-Cheong Wong, and Frank Eisenhaber
Part IV: Big Data
26. Big Data, Evolution, and Metagenomes: Predicting Disease from Gut Microbiota Codon Usage Profiles
Maja Fabijanic and Kristian Vlahoviček
27. Big Data in Plant Science: Resources and Data Mining Tools for Plant Genomics and Prote
Автор: Oliviero Carugo; Frank Eisenhaber Название: Data Mining Techniques for the Life Sciences ISBN: 1493956884 ISBN-13(EAN): 9781493956883 Издательство: Springer Рейтинг: Цена: 20263.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: In this book, experts in the field contribute valuable information about the sources of information and the techniques used for "mining" new insights out of databases. The book covers a wide range of biological systems and in silico approaches.
Автор: Samantha Kleinberg Название: Time and Causality Across the Sciences ISBN: 1108476678 ISBN-13(EAN): 9781108476676 Издательство: Cambridge Academ Рейтинг: Цена: 9186.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides an entry point for researchers in any field, bringing together perspectives collected from a large body of work on causality across disciplines. Topics include whether quantum mechanics allows causes to precede their effects, the integration of mechanisms, and insight into the role played by intervention and timing information.
Описание: Like the popular second edition, Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining?including both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. <br><br>Complementing the book is a fully functional platform-independent open source Weka software for machine learning, available for free download. <br><br>The book is a major revision of the second edition that appeared in 2005. While the basic core remains the same, it has been updated to reflect the changes that have taken place over the last four or five years. The highlights for the updated new edition include completely revised technique sections; new chapter on Data Transformations, new chapter on Ensemble Learning, new chapter on Massive Data Sets, a new ?book release? version of the popular Weka machine learning open source software (developed by the authors and specific to the Third Edition); new material on ?multi-instance learning?; new information on ranking the classification, plus comprehensive updates and modernization throughout. All in all, approximately 100 pages of new material.<br> <br><br>* Thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques<br><br>* Algorithmic methods at the heart of successful data mining?including tired and true methods as well as leading edge methods<br><br>* Performance improvement techniques that work by transforming the input or output<br><br>* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization?in an updated, interactive interface. <br>
Автор: Jiawei Han Название: Data Mining: Concepts and Techniques, ISBN: 0123814790 ISBN-13(EAN): 9780123814791 Издательство: Elsevier Science Рейтинг: Цена: 9720.00 р. Наличие на складе: Поставка под заказ.
Описание: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining.
Описание: Biologically-inspired data mining has a wide variety of applications in areas such as data clustering, classification, sequential pattern mining, and information extraction in healthcare and bioinformatics. Over the past decade, research materials in this area have dramatically increased, providing clear evidence of the popularity of these techniques.Biologically-Inspired Techniques for Knowledge Discovery and Data Mining exemplifies prestigious research and shares the practices that have allowed these areas to grow and flourish. This essential reference publication highlights contemporary findings in the area of biologically-inspired techniques in data mining domains and their implementation in real-life problems. Providing quality work from established researchers, this publication serves to extend existing knowledge within the research communities of data mining and knowledge discovery, as well as for academicians and students in the field.
Описание: Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and manageing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios.Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.
Описание: Data warehousing is an important topic that is of interest to both the industry and the knowledge engineering research communities. Both data mining and data warehousing technologies have similar objectives and can potentially benefit from each other’s methods to facilitate knowledge discovery.Improving Knowledge Discovery through the Integration of Data Mining Techniques provides insight concerning the integration of data mining and data warehousing for enhancing the knowledge discovery process. Decision makers, academicians, researchers, advanced-level students, technology developers, and business intelligence professionals will find this book useful in furthering their research exposure to relevant topics in knowledge discovery.
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated fourth edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https: //www.cs.waikato.ac.nz/ ml/weka/book.html.
It contains
Powerpoint slides for Chapters 1-12. This is a very comprehensive teaching resource, with many PPT slides covering each chapter of the book
Online Appendix on the Weka workbench; again a very comprehensive learning aid for the open source software that goes with the book
Table of contents, highlighting the many new sections in the 4th edition, along with reviews of the 1st edition, errata, etc.
Provides a thorough grounding in machine learning concepts, as well as practical advice on applying the tools and techniques to data mining projects
Presents concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods
Includes a downloadable WEKA software toolkit, a comprehensive collection of machine learning algorithms for data mining tasks-in an easy-to-use interactive interface
Includes open-access online courses that introduce practical applications of the material in the book
Описание: Data mining is an important branch of computer science and information technology management that deals with the discovery and analysis of datasets. This book covers in detail some existent theories as well as innovative concepts revolving around data mining such as bio data analytics, analysis of social structures and patterns, correlations and fluctuations, etc. With its detailed analyses and data, this book will prove immensely beneficial to professionals and students involved in this area at various levels.
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