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Introduction to Data Mining for the Life Sciences

Author: Rob Sullivan

Publisher: Springer Science & Business Media

ISBN:

Category: Science

Page: 638

View: 790

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.

Genome Exploitation

Data Mining the Genome

Author: J. Perry Gustafson

Publisher: Springer Science & Business Media

ISBN:

Category: Science

Page: 252

View: 785

Genome Exploitation: Data Mining the Genome is developed from the 23rd Stadler Genetic Symposium. This volume discusses and illustrates how scientists are going to characterize and make use of the massive amount of information being accumulated about the plant and animal genomes. Genome Exploitation: Data Mining the Genome is a state-of-the-art picture on mining the Genome databases. This is one of the few times that researchers in both plants and animals will be working together to create a seminal data resource.

Handbook of Research on Computational Grid Technologies for Life Sciences, Biomedicine, and Healthcare

Author: Cannataro, Mario

Publisher: IGI Global

ISBN:

Category: Medical

Page: 960

View: 661

"This book provides methodologies and developments of grid technologies applied in different fields of life sciences"--Provided by publisher.

Cluster Analysis and Data Mining

An Introduction

Author: Ronald S. King

Publisher: Stylus Publishing, LLC

ISBN:

Category: Computers

Page: 300

View: 306

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 Integration in the Life Sciences

10th International Conference, DILS 2014, Lisbon, Portugal, July 17-18, 2014. Proceedings

Author: Helena Galhardas

Publisher: Springer

ISBN:

Category: Computers

Page: 151

View: 567

This book constitutes the refereed proceedings of the 10th International Conference on Data Integration in the Life Sciences, DILS 2014, held in Lisbon, Portugal, in July 2014. The 9 revised full papers and the 5 short papers included in this volume were carefully reviewed and selected from 20 submissions. The papers cover a range of important topics such as data integration platforms and applications; biodiversity data management; ontologies and visualization; linked data and query processing.

Data Mining Applications for Empowering Knowledge Societies

Author: Rahman, Hakikur

Publisher: IGI Global

ISBN:

Category: Technology & Engineering

Page: 356

View: 541

Presents an overview of the main issues of data mining, including its classification, regression, clustering, and ethical issues. Provides readers with knowledge enhancing processes as well as a wide spectrum of data mining applications.

Advances in Knowledge Discovery and Data Mining

12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings

Author: Takashi Washio

Publisher: Springer

ISBN:

Category: Computers

Page: 1102

View: 693

ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held every year since 1997. PAKDD 2008, the 12th in the series, was heldatOsaka,JapanduringMay20–23,2008.PAKDDisaleadinginternational conference in the area of data mining. It provides an international forum for - searchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD-related areas - cluding data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scienti?c discovery, data visualization, causal induction, and knowledge-based systems. This year we received a total of 312 research papers from 34 countries and regions in Asia, Australia, North America, South America, Europe, and Africa. Every submitted paper was rigorously reviewed by two or three reviewers, d- cussed by the reviewers under the supervision of an Area Chair, and judged by the Program Committee Chairs. When there was a disagreement, the Area Chair and/or the Program Committee Chairs provided an additional review. Thus, many submissions were reviewed by four experts. The Program Comm- tee members were deeply involved in a highly selective process. As a result, only approximately11.9%ofthe312submissionswereacceptedaslongpapers,12.8% of them were accepted as regular papers, and 11.5% of them were accepted as short papers.

Next Generation of Data-Mining Applications

Author: Mehmed Kantardzic

Publisher: Wiley-IEEE Press

ISBN:

Category: Computers

Page: 671

View: 486

Discover the next generation of data-mining tools and technology This book brings together an international team of eighty experts to present readers with the next generation of data-mining applications. Unlike other publications that take a strictly academic and theoretical approach, this book features authors who have successfully developed data-mining solutions for a variety of customer types. Presenting their state-of-the-art methodologies and techniques, the authors show readers how they can analyze enormous quantities of data and make new discoveries by connecting key pieces of data that may be spread across several different databases and file servers. The latest data-mining techniques that will revolutionize research across a wide variety of fields including business, science, healthcare, and industry are all presented. Organized by application, the twenty-five chapters cover applications in: Industry and business Science and engineering Bioinformatics and biotechnology Medicine and pharmaceuticals Web and text-mining Security New trends in data-mining technology And much more . . . Readers from a variety of disciplines will learn how the next generation of data-mining applications can radically enhance their ability to analyze data and open the doors to new opportunities. Readers will discover: New data-mining tools to automate the evaluation and qualification of sales opportunities The latest tools needed for gene mapping and proteomic data analysis Sophisticated techniques that can be engaged in crime fighting and prevention With its coverage of the most advanced applications, Next Generation of Data-Mining Applications is essential reading for all researchers working in data mining or who are tasked with making sense of an ever-growing quantity of data. The publication also serves as an excellent textbook for upper-level undergraduate and graduate courses in computer science, information management, and statistics.

Scientific and Statistical Database Management

20th International Conference, SSDBM 2008, Hong Kong, China, July 9-11, 2008, Proceedings

Author: Bertram Ludäscher

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 620

View: 996

This book constitutes the refereed proceedings of the 20th International Conference on Scientific and Statistical Database Management, SSDBM 2008, held in Hong Kong, China, in July 2008. The 28 revised full papers, 7 revised short papers and 8 poster and demo papers presented together with 3 invited talks were carefully reviewed and selected from 84 submissions. The papers are organized in topical sections on query optimization in scientific databases, privacy, searching and mining graphs, data streams, scientific database applications, advanced indexing methods, data mining, as well as advanced queries and uncertain data.

Fundamentals of Data Mining in Genomics and Proteomics

Author: Werner Dubitzky

Publisher: Springer Science & Business Media

ISBN:

Category: Science

Page: 281

View: 344

This book presents state-of-the-art analytical methods from statistics and data mining for the analysis of high-throughput data from genomics and proteomics. It adopts an approach focusing on concepts and applications and presents key analytical techniques for the analysis of genomics and proteomics data by detailing their underlying principles, merits and limitations.

Enabling Real-Time Business Intelligence

6th International Workshop, BIRTE 2012, Held at the 38th International Conference on Very Large Databases, VLDB 2012, Istanbul, Turkey, August 27, 2012, Revised Selected Papers

Author: Malu Castellanos

Publisher: Springer

ISBN:

Category: Computers

Page: 145

View: 655

This book constitutes the thoroughly refereed conference proceedings of the 6th International Workshop on Business Intelligence for the Real-Time Enterprise, BIRTE 2012, held in Istanbul, Turkey, in August 2012, in conjunction with VLDB 2012, the International Conference on Very Large Data Bases. The BIRTE workshop series provides a forum to discuss and advance the science and engineering enabling real-time business intelligence and the novel applications that build on these foundational techniques. This volume contains ten research papers, which were carefully reviewed and selected from 13 submissions.

Data Mining in Bioinformatics

Author: Jason T. L. Wang

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 340

View: 705

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 in Biomedicine

Author: Panos M. Pardalos

Publisher: Springer Science & Business Media

ISBN:

Category: Medical

Page: 582

View: 843

This volume presents an extensive collection of contributions covering aspects of the exciting and important research field of data mining techniques in biomedicine. Coverage includes new approaches for the analysis of biomedical data; applications of data mining techniques to real-life problems in medical practice; comprehensive reviews of recent trends in the field. The book addresses incorporation of data mining in fundamental areas of biomedical research: genomics, proteomics, protein characterization, and neuroscience.

Computational Models For Life Sciences (CMLS '07)

2007 International Symposium on Computational Models of Life Sciences

Author: Tuan D. Pham

Publisher: American Institute of Physics

ISBN:

Category: Computers

Page: 362

View: 510

This conference proceedings text features research papers that address novel applications of computer, physical, engineering and mathematical models for solving modern challenging problems in life sciences. All the papers, presented at the Computational Models for Life Sciences conference held in 2007, have been peer-reviewed. They cover a huge range of topics, including image analysis, computer vision, and pattern analysis and classification, among many others.

2011 International Conference in Electrics, Communication and Automatic Control Proceedings

Author: Ran Chen

Publisher: Springer Science & Business Media

ISBN:

Category: Technology & Engineering

Page: 1746

View: 916

2011 International Conference in Electrics, Communication and Automatic Control Proceedings examines state-of-art and advances in Electrics, Communication and Automatic Control. This book presents developments in Power Conversion, Signal and image processing, Image & video Signal Processing. The conference brings together researchers, engineers, academic as well as industrial professionals from all over the world to promote the developments of Electrics, Communication and Automatic Control.

Data Mining for Genomics and Proteomics

Analysis of Gene and Protein Expression Data

Author: Darius M. Dziuda

Publisher: John Wiley & Sons

ISBN:

Category: Computers

Page: 328

View: 859

Data Mining for Genomics and Proteomics uses pragmatic examples and a complete case study to demonstrate step-by-step how biomedical studies can be used to maximize the chance of extracting new and useful biomedical knowledge from data. It is an excellent resource for students and professionals involved with gene or protein expression data in a variety of settings.

Data Integration in the Life Sciences

5th International Workshop, DILS 2008, Evry, France, June 25-27, 2008, Proceedings

Author: Amos Bairoch

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 209

View: 372

This book constitutes the refereed proceedings of the 5th International Workshop on Data Integration in the Life Sciences, DILS 2008, held in Evry, France in June 2008. The 18 revised full papers presented together with 3 keynote talks and a tutorial paper were carefully reviewed and selected from 54 submissions. The papers adress all current issues in data integration and data management from the life science point of view and are organized in topical sections on Semantic Web for the life sciences, designing and evaluating architectures to integrate biological data, new architectures and experience on using systems, systems using technologies from the Semantic Web for the life sciences, mining integrated biological data, and new features of major resources for biomolecular data.

Interactive Knowledge Discovery and Data Mining in Biomedical Informatics

State-of-the-Art and Future Challenges

Author: Andreas Holzinger

Publisher: Springer

ISBN:

Category: Computers

Page: 357

View: 785

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of = 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning./ppThis state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.

Introduction to Data Mining and Its Applications

Author: S. Sumathi

Publisher: Springer Science & Business Media

ISBN:

Category: Computers

Page: 828

View: 782

This book explores the concepts of data mining and data warehousing, a promising and flourishing frontier in data base systems and new data base applications and is also designed to give a broad, yet in-depth overview of the field of data mining. Data mining is a multidisciplinary field, drawing work from areas including database technology, AI, machine learning, NN, statistics, pattern recognition, knowledge based systems, knowledge acquisition, information retrieval, high performance computing and data visualization. This book is intended for a wide audience of readers who are not necessarily experts in data warehousing and data mining, but are interested in receiving a general introduction to these areas and their many practical applications. Since data mining technology has become a hot topic not only among academic students but also for decision makers, it provides valuable hidden business and scientific intelligence from a large amount of historical data. It is also written for technical managers and executives as well as for technologists interested in learning about data mining.

Data Integration in the Life Sciences

9th International Conference, DILS 2013, Montreal, Canada, July 11-12, 2013, Proceedings

Author: Christopher J.O. Baker

Publisher: Springer

ISBN:

Category: Computers

Page: 141

View: 839

This book constitutes the refereed proceedings of the 9th International Conference on Data Integration in the Life Sciences, DILS 2013, held in Montreal, QC, Canada, in July 2013. The 10 revised papers included in this volume were carefully reviewed and selected from 23 submissions. The papers cover a range of important topics such as algorithms for ontology matching, interoperable frameworks for text mining using semantic web services, pipelines for genome-wide functional annotation, automation of pipelines providing data discovery and access to distributed resources, knowledge-driven querying-answer systems, prizms, nanopublications, electronic health records and linked data.

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