Knowledge Discovery In Bioinformatics PDF Download
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Author | : Xiaohua Hu |
Publisher | : John Wiley & Sons |
Total Pages | : 400 |
Release | : 2007-06-11 |
Genre | : Technology & Engineering |
ISBN | : 9780470124635 |
Download Knowledge Discovery in Bioinformatics Book in PDF, ePub and Kindle
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.
Author | : Akil Z. Surti |
Publisher | : Lulu.com |
Total Pages | : 92 |
Release | : |
Genre | : |
ISBN | : 0359609589 |
Download KNOWLEDGE DISCOVERY IN BIOINFORMATICS Book in PDF, ePub and Kindle
Author | : Gil Alterovitz |
Publisher | : John Wiley & Sons |
Total Pages | : 306 |
Release | : 2011-04-20 |
Genre | : Medical |
ISBN | : 1119995833 |
Download Knowledge-Based Bioinformatics Book in PDF, ePub and Kindle
There is an increasing need throughout the biomedical sciences for a greater understanding of knowledge-based systems and their application to genomic and proteomic research. This book discusses knowledge-based and statistical approaches, along with applications in bioinformatics and systems biology. The text emphasizes the integration of different methods for analysing and interpreting biomedical data. This, in turn, can lead to breakthrough biomolecular discoveries, with applications in personalized medicine. Key Features: Explores the fundamentals and applications of knowledge-based and statistical approaches in bioinformatics and systems biology. Helps readers to interpret genomic, proteomic, and metabolomic data in understanding complex biological molecules and their interactions. Provides useful guidance on dealing with large datasets in knowledge bases, a common issue in bioinformatics. Written by leading international experts in this field. Students, researchers, and industry professionals with a background in biomedical sciences, mathematics, statistics, or computer science will benefit from this book. It will also be useful for readers worldwide who want to master the application of bioinformatics to real-world situations and understand biological problems that motivate algorithms.
Author | : Jake Y. Chen |
Publisher | : CRC Press |
Total Pages | : 736 |
Release | : 2009-09-01 |
Genre | : Computers |
ISBN | : 1420086855 |
Download Biological Data Mining Book in PDF, ePub and Kindle
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 interdisciplin
Author | : Mourad Elloumi |
Publisher | : John Wiley & Sons |
Total Pages | : 1126 |
Release | : 2015-02-04 |
Genre | : Computers |
ISBN | : 1118853725 |
Download Biological Knowledge Discovery Handbook Book in PDF, ePub and Kindle
The first comprehensive overview of preprocessing, mining, and postprocessing of biological data Molecular biology is undergoing exponential growth in both the volume and complexity of biological data and knowledge discovery offers the capacity to automate complex search and data analysis tasks. This book presents a vast overview of the most recent developments on techniques and approaches in the field of biological knowledge discovery and data mining (KDD) providing in-depth fundamental and technical field information on the most important topics encountered. Written by top experts, Biological Knowledge Discovery Handbook: Preprocessing, Mining, and Postprocessing of Biological Data covers the three main phases of knowledge discovery (data preprocessing, data processing also known as data mining and data postprocessing) and analyzes both verification systems 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 Data Mining Combining sound theory with practical applications in molecular biology, Biological Knowledge Discovery Handbook is ideal for courses in bioinformatics and biological KDD as well as for practitioners and professional researchers in computer science, life science, and mathematics.
Author | : Igor Jurisica |
Publisher | : CRC Press |
Total Pages | : 360 |
Release | : 2005-09-02 |
Genre | : Computers |
ISBN | : 1420035169 |
Download Knowledge Discovery in Proteomics Book in PDF, ePub and Kindle
Multi-modal representations, the lack of complete and consistent domain theories, rapid evolution of domain knowledge, high dimensionality, and large amounts of missing information - these are challenges inherent in modern proteomics. As our understanding of protein structure and function becomes ever more complicated, we have reached a point where
Author | : Karl Tuyls |
Publisher | : Springer |
Total Pages | : 191 |
Release | : 2007-05-05 |
Genre | : Science |
ISBN | : 354071037X |
Download Knowledge Discovery and Emergent Complexity in Bioinformatics Book in PDF, ePub and Kindle
This book constitutes the thoroughly refereed post-proceedings of the First International Workshop on Knowledge Discovery and Emergent Complexity in Bioinformatics, KDECB 2006, held in Ghent, Belgium, in May 2006, in connection with the 15th Belgium-Netherlands Conference on Machine Learning. The 12 revised full papers cover various topics in the areas of knowledge discovery and emergent complexity research in bioinformatics.
Author | : Li, Xiao-Li |
Publisher | : IGI Global |
Total Pages | : 464 |
Release | : 2012-06-30 |
Genre | : Medical |
ISBN | : 1466617861 |
Download Computational Knowledge Discovery for Bioinformatics Research Book in PDF, ePub and Kindle
"This book discusses the most significant research and latest practices in computational knowledge discovery approaches to bioinformatics in a cross-disciplinary manner that is useful for researchers, practitioners, academicians, mathematicians, statisticians, and computer scientists involved in the many facets of bioinformatics"--
Author | : Andreas Holzinger |
Publisher | : Springer |
Total Pages | : 373 |
Release | : 2014-06-17 |
Genre | : Computers |
ISBN | : 3662439689 |
Download Interactive Knowledge Discovery and Data Mining in Biomedical Informatics Book in PDF, ePub and Kindle
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.
Author | : Sumeet Dua |
Publisher | : CRC Press |
Total Pages | : 351 |
Release | : 2012-11-06 |
Genre | : Computers |
ISBN | : 0849328012 |
Download Data Mining for Bioinformatics Book in PDF, ePub and Kindle
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.