Download data mining for bioinformatics applications in pdf or read data mining for bioinformatics applications in pdf online books in PDF, EPUB and Mobi Format. Click Download or Read Online button to get data mining for bioinformatics applications in pdf book now. This site is like a library, Use search box in the widget to get ebook that you want.



Data Mining For Bioinformatics Applications

Author: He Zengyou
Publisher: Woodhead Publishing
ISBN: 008100107X
Size: 38.81 MB
Format: PDF, Kindle
View: 2324
Download and Read
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

Data Mining For Bioinformatics

Author: Sumeet Dua
Publisher: CRC Press
ISBN: 0849328012
Size: 13.96 MB
Format: PDF, Kindle
View: 1899
Download and Read
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 help readers from both biology and computer science backgrounds gain an enhanced understanding of this cross-disciplinary field. The book offers authoritative coverage of data mining techniques, technologies, and frameworks used for storing, analyzing, and extracting knowledge from large databases in the bioinformatics domains, including genomics and proteomics. It begins by describing the evolution of bioinformatics and highlighting the challenges that can be addressed using data mining techniques. Introducing the various data mining techniques that can be employed in biological databases, the text is organized into four sections: Supplies a complete overview of the evolution of the field and its intersection with computational learning Describes the role of data mining in analyzing large biological databases—explaining the breath of the various feature selection and feature extraction techniques that data mining has to offer Focuses on concepts of unsupervised learning using clustering techniques and its application to large biological data Covers supervised learning using classification techniques most commonly used in bioinformatics—addressing the need for validation and benchmarking of inferences derived using either clustering or classification The book describes the various biological databases prominently referred to in bioinformatics and includes a detailed list of the applications of advanced clustering algorithms used in bioinformatics. Highlighting the challenges encountered during the application of classification on biological databases, it considers systems of both single and ensemble classifiers and shares effort-saving tips for model selection and performance estimation strategies.

Multiobjective Genetic Algorithms For Clustering

Author: Ujjwal Maulik
Publisher: Springer Science & Business Media
ISBN: 3642166156
Size: 53.88 MB
Format: PDF, Docs
View: 6105
Download and Read
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.

Advanced Data Mining Technologies In Bioinformatics

Author: Hsu, Hui-Huang
Publisher: IGI Global
ISBN: 1591408652
Size: 44.74 MB
Format: PDF, Mobi
View: 742
Download and Read
"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.

Knowledge Discovery In Bioinformatics

Author: Xiaohua Hu
Publisher: John Wiley & Sons
ISBN: 9780470124635
Size: 62.57 MB
Format: PDF, ePub, Mobi
View: 1398
Download and Read
The purpose of this edited book is to bring together the ideas and findings of data mining researchers and bioinformaticians by discussing cutting-edge research topics such as, gene expressions, protein/RNA structure prediction, phylogenetics, sequence and structural motifs, genomics and proteomics, gene findings, drug design, RNAi and microRNA analysis, text mining in bioinformatics, modelling of biochemical pathways, biomedical ontologies, system biology and pathways, and biological database management.

Bioinformatics

Author: M. H. Fulekar
Publisher: Springer Science & Business Media
ISBN: 1402088809
Size: 41.72 MB
Format: PDF, ePub, Mobi
View: 3535
Download and Read
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.

Biological Data Mining

Author: Jake Y. Chen
Publisher: CRC Press
ISBN: 9781420086850
Size: 66.67 MB
Format: PDF, Kindle
View: 4039
Download and Read
Like a data-guzzling turbo engine, advanced data mining has been powering post-genome biological studies for two decades. Reflecting this growth, Biological Data Mining presents comprehensive data mining concepts, theories, and applications in current biological and medical research. Each chapter is written by a distinguished team of interdisciplinary data mining researchers who cover state-of-the-art biological topics. The first section of the book discusses challenges and opportunities in analyzing and mining biological sequences and structures to gain insight into molecular functions. The second section addresses emerging computational challenges in interpreting high-throughput Omics data. The book then describes the relationships between data mining and related areas of computing, including knowledge representation, information retrieval, and data integration for structured and unstructured biological data. The last part explores emerging data mining opportunities for biomedical applications. This volume examines the concepts, problems, progress, and trends in developing and applying new data mining techniques to the rapidly growing field of genome biology. By studying the concepts and case studies presented, readers will gain significant insight and develop practical solutions for similar biological data mining projects in the future.

Data Mining And Applications In Genomics

Author: Sio-Iong Ao
Publisher: Springer Science & Business Media
ISBN: 1402089759
Size: 32.14 MB
Format: PDF, Docs
View: 7628
Download and Read
Data Mining and Applications in Genomics contains the data mining algorithms and their applications in genomics, with frontier case studies based on the recent and current works at the University of Hong Kong and the Oxford University Computing Laboratory, University of Oxford. It provides a systematic introduction to the use of data mining algorithms as an investigative tool for applications in genomics. Data Mining and Applications in Genomics offers state of the art of tremendous advances in data mining algorithms and applications in genomics and also serves as an excellent reference work for researchers and graduate students working on data mining algorithms and applications in genomics.

Data Mining In Bioinformatics

Author: Jason T. L. Wang
Publisher: Springer Science & Business Media
ISBN: 1846280591
Size: 78.11 MB
Format: PDF
View: 3310
Download and Read
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.

Contrast Data Mining

Author: Guozhu Dong
Publisher: CRC Press
ISBN: 1439854335
Size: 36.67 MB
Format: PDF, Mobi
View: 5159
Download and Read
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.