data mining for bioinformatics applications

Data Mining For Bioinformatics Applications
Author: He Zengyou
Publisher: Woodhead Publishing
Release Date: 2015-06-09
Pages: 100
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

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

Advanced Data Mining Technologies In Bioinformatics
Author: Hsu, Hui-Huang
Publisher: IGI Global
Release Date: 2006-03-31
Pages: 342
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

"This book covers research topics of data mining on bioinformatics presenting the basics and problems of bioinformatics and applications of data mining technologies pertaining to the field"--Provided by publisher.

Data Mining For Scientific And Engineering Applications
Author: R.L. Grossman
Publisher: Springer Science & Business Media
Release Date: 2001-10-31
Pages: 605
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Advances in technology are making massive data sets common in many scientific disciplines, such as astronomy, medical imaging, bio-informatics, combinatorial chemistry, remote sensing, and physics. To find useful information in these data sets, scientists and engineers are turning to data mining techniques. This book is a collection of papers based on the first two in a series of workshops on mining scientific datasets. It illustrates the diversity of problems and application areas that can benefit from data mining, as well as the issues and challenges that differentiate scientific data mining from its commercial counterpart. While the focus of the book is on mining scientific data, the work is of broader interest as many of the techniques can be applied equally well to data arising in business and web applications. Audience: This work would be an excellent text for students and researchers who are familiar with the basic principles of data mining and want to learn more about the application of data mining to their problem in science or engineering.

Multiobjective Genetic Algorithms For Clustering
Author: Ujjwal Maulik
Publisher: Springer Science & Business Media
Release Date: 2011-09-01
Pages: 281
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

This is the first book primarily dedicated to clustering using multiobjective genetic algorithms with extensive real-life applications in data mining and bioinformatics. The authors first offer detailed introductions to the relevant techniques – genetic algorithms, multiobjective optimization, soft computing, data mining and bioinformatics. They then demonstrate systematic applications of these techniques to real-world problems in the areas of data mining, bioinformatics and geoscience. The authors offer detailed theoretical and statistical notes, guides to future research, and chapter summaries. The book can be used as a textbook and as a reference book by graduate students and academic and industrial researchers in the areas of soft computing, data mining, bioinformatics and geoscience.

Data Mining For Bioinformatics
Author: Sumeet Dua
Publisher: CRC Press
Release Date: 2012-11-06
Pages: 348
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

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 he

Bioinformatics
Author: M. H. Fulekar
Publisher: Springer Science & Business Media
Release Date: 2009-03-24
Pages: 247
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Bioinformatics, computational biology, is a relatively new field that applies computer science and information technology to biology. In recent years, the discipline of bioinformatics has allowed biologists to make full use of the advances in Computer sciences and Computational statistics for advancing the biological data. Researchers in life sciences generate, collect and need to analyze an increasing number of different types of scientific data, DNA, RNA and protein sequences, in-situ and microarray gene expression including 3D protein structures and biological pathways. This book is aiming to provide information on bioinformatics at various levels. The chapters included in this book cover introductory to advanced aspects, including applications of various documented research work and specific case studies related to bioinformatics. This book will be of immense value to readers of different backgrounds such as engineers, scientists, consultants and policy makers for industry, government, academics and social and private organisations.

Fuzzy Systems And Knowledge Discovery
Author: Lipo Wang
Publisher: Springer Science & Business Media
Release Date: 2006-09-19
Pages: 1340
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

This book constitutes the refereed proceedings of the Third International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2006, held in federation with the Second International Conference on Natural Computation ICNC 2006. The book presents 115 revised full papers and 50 revised short papers. Coverage includes neural computation, quantum computation, evolutionary computation, DNA computation, fuzzy computation, granular computation, artificial life, innovative applications to knowledge discovery, finance, operations research, and more.

Data Mining In Bioinformatics
Author: Jason T. L. Wang
Publisher: Springer Science & Business Media
Release Date: 2006-03-30
Pages: 340
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

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.

Bioinformatics Technologies
Author: Yi-Ping Phoebe Chen
Publisher: Springer Science & Business Media
Release Date: 2005-01-18
Pages: 396
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Introductio to bioinformatics. Overview of structural bioinformatics. Database warehousing in bioinformatics. Modeling for bioinformatics. Pattern matching for motifs. Visualization and fractal analysis of biological sequences. Microarray data analysis.

Evolutionary Computation  Machine Learning And Data Mining In Bioinformatics
Author: Clara Pizzuti
Publisher: Springer Science & Business Media
Release Date: 2010-03-25
Pages: 249
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

The ?eld of bioinformatics has two main objectives: the creation and main- nance of biological databases, and the discovery of knowledge from life sciences datainordertounravelthemysteriesofbiologicalfunction,leadingtonewdrugs andtherapiesforhumandisease. Life sciencesdatacomeinthe formofbiological sequences, structures, pathways, or literature. One major aspect of discovering biological knowledge is to search, predict, or model speci?c information in a given dataset in order to generate new interesting knowledge. Computer science methods such as evolutionary computation, machine learning, and data mining all have a great deal to o?er the ?eld of bioinformatics. The goal of the 8th - ropean Conference on Evolutionary Computation, Machine Learning, and Data Mining in Bioinformatics (EvoBIO 2010) was to bring together experts in these ?elds in order to discuss new and novel methods for tackling complex biological problems. The 8th EvoBIO conference was held in Istanbul, Turkey during April 7–9, 2010attheIstanbulTechnicalUniversity. EvoBIO2010washeldjointlywiththe 13th European Conference on Genetic Programming (EuroGP 2010), the 10th European Conference on Evolutionary Computation in Combinatorial Opti- sation (EvoCOP 2010), and the conference on the applications of evolutionary computation,EvoApplications. Collectively,the conferences areorganizedunder the name Evo* (www. evostar. org). EvoBIO, held annually as a workshop since 2003, became a conference in 2007 and it is now the premiere European event for those interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology.

Evolutionary Computation  Machine Learning And Data Mining In Bioinformatics
Author: Elena Marchiori
Publisher: Springer Science & Business Media
Release Date: 2008-03-14
Pages: 211
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

This book constitutes the refereed proceedings of the 6th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2008, held in Naples, Italy, in March 2008 colocated with the Evo* 2008 events. The 18 revised full papers were carefully reviewed and selected from 63 submissions. EvoBio is the premiere European event for experts in computer science meeting with experts in bioinformatics and the biological sciences, all interested in the interface between evolutionary computation, machine learning, data mining, bioinformatics, and computational biology. Topics addressed by the papers include biomarker discovery, cell simulation and modeling, ecological modeling, uxomics, gene networks, biotechnology, metabolomics, microarray analysis, phylogenetics, protein interactions, proteomics, sequence analysis and alignment, as well as systems biology.

Data Mining
Author: Sushmita Mitra
Publisher: John Wiley & Sons
Release Date: 2005-01-21
Pages: 424
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

First title to ever present soft computing approaches and their application in data mining, along with the traditional hard-computing approaches Addresses the principles of multimedia data compression techniques (for image, video, text) and their role in data mining Discusses principles and classical algorithms on string matching and their role in data mining

Bioinformatics
Author: David Edwards
Publisher: Springer Science & Business Media
Release Date: 2009-09-03
Pages: 451
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

Bioinformatics is a relatively new field of research. It evolved from the requirement to process, characterize, and apply the information being produced by DNA sequencing technology. The production of DNA sequence data continues to grow exponentially. At the same time, improved bioinformatics such as faster DNA sequence search methods have been combined with increasingly powerful computer systems to process this information. Methods are being developed for the ever more detailed quantification of gene expression, providing an insight into the function of the newly discovered genes, while molecular genetic tools provide a link between these genes and heritable traits. Genetic tests are now available to determine the likelihood of suffering specific ailments and can predict how plant cultivars may respond to the environment. The steps in the translation of the genetic blueprint to the observed phenotype is being increasingly understood through proteome, metabolome and phenome analysis, all underpinned by advances in bioinformatics. Bioinformatics is becoming increasingly central to the study of biology, and a day at a computer can often save a year or more in the laboratory. The volume is intended for graduate-level biology students as well as researchers who wish to gain a better understanding of applied bioinformatics and who wish to use bioinformatics technologies to assist in their research. The volume would also be of value to bioinformatics developers, particularly those from a computing background, who would like to understand the application of computational tools for biological research. Each chapter would include a comprehensive introduction giving an overview of the fundamentals, aimed at introducing graduate students and researchers from diverse backgrounds to the field and bring them up-to-date on the current state of knowledge. To accommodate the broad range of topics in applied bioinformatics, chapters have been grouped into themes: gene and genome analysis, molecular genetic analysis, gene expression analysis, protein and proteome analysis, metabolome analysis, phenome data analysis, literature mining and bioinformatics tool development. Each chapter and theme provides an introduction to the biology behind the data describes the requirements for data processing and details some of the methods applied to the data to enhance biological understanding.

Contrast Data Mining
Author: Guozhu Dong
Publisher: CRC Press
Release Date: 2016-04-19
Pages: 434
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. Learn from Real Case Studies of Contrast Mining Applications In this volume, researchers from around the world specializing in architecture engineering, bioinformatics, computer science, medicine, and systems engineering focus on the mining and use of contrast patterns. They demonstrate many useful and powerful capabilities of a variety of contrast mining techniques and algorithms, including tree-based structures, zero-suppressed binary decision diagrams, data cube representations, and clustering algorithms. They also examine how contrast mining is used in leukemia characterization, discriminative gene transfer and microarray analysis, computational toxicology, spatial and image data classification, voting analysis, heart disease prediction, crime analysis, understanding customer behavior, genetic algorithms, and network security.

Data Warehousing And Mining  Concepts  Methodologies  Tools  And Applications
Author: Wang, John
Publisher: IGI Global
Release Date: 2008-05-31
Pages: 4092
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

In recent years, the science of managing and analyzing large datasets has emerged as a critical area of research. In the race to answer vital questions and make knowledgeable decisions, impressive amounts of data are now being generated at a rapid pace, increasing the opportunities and challenges associated with the ability to effectively analyze this data.

Data Mining And Bioinformatics
Author: Mehmet M Dalkilic
Publisher: Springer Science & Business Media
Release Date: 2006-12-21
Pages: 195
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

This book constitutes the thoroughly refereed post-proceedings of the First VLDB 2006 International Workshop on Data Mining and Bioinformatics, VDMB 2006, held in Seoul, Korea in September 2006 in conjunction with VLDB 2006. The 15 revised full papers presented together with an invited talk were carefully reviewed and selected from 30 submissions. The papers cover various topics in the areas of microarray data analysis, bioinformatics system and text retrieval, application of gene expression data, and sequence analysis.

XML Data Mining  Models  Methods  And Applications
Author: Tagarelli, Andrea
Publisher: IGI Global
Release Date: 2011-11-30
Pages: 538
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

The widespread use of XML in business and scientific databases has prompted the development of methodologies, techniques, and systems for effectively managing and analyzing XML data. This has increasingly attracted the attention of different research communities, including database, information retrieval, pattern recognition, and machine learning, from which several proposals have been offered to address problems in XML data management and knowledge discovery. XML Data Mining: Models, Methods, and Applications aims to collect knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods, and systems for XML data mining. This book addresses key issues and challenges in XML data mining, offering insights into the various existing solutions and best practices for modeling, processing, analyzing XML data, and for evaluating performance of XML data mining algorithms and systems.

Evolutionary Computation  Machine Learning And Data Mining In Bioinformatics
Author: Mario Giacobini
Publisher: Springer Science & Business Media
Release Date: 2012-03-28
Pages: 255
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

This book constitutes the refereed proceedings of the 10th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics, EvoBIO 2012, held in Málaga, Spain, in April 2012 co-located with the Evo* 2012 events. The 15 revised full papers presented together with 8 poster papers were carefully reviewed and selected from numerous submissions. Computational Biology is a wide and varied discipline, incorporating aspects of statistical analysis, data structure and algorithm design, machine learning, and mathematical modeling toward the processing and improved understanding of biological data. Experimentalists now routinely generate new information on such a massive scale that the techniques of computer science are needed to establish any meaningful result. As a consequence, biologists now face the challenges of algorithmic complexity and tractability, and combinatorial explosion when conducting even basic analyses.

Machine Learning In Bioinformatics
Author: Yanqing Zhang
Publisher: John Wiley & Sons
Release Date: 2009-02-23
Pages: 400
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

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.

Biological Data Mining And Its Applications In Healthcare
Author: Xiao-Li Li
Publisher: World Scientific Publishing Company Incorporated
Release Date: 2013-11
Pages: 420
ISBN:
Available Language: English, Spanish, And French
EBOOK SYNOPSIS:

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.