XML for Bioinformatics
XML for Bioinformatics

Author: Ethan Cerami

Publisher: Springer Science & Business Media

Published: 2006-06-02

Total Pages: 311

ISBN-13: 0387274782

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Introduction The goal of this book is to introduce XML to a bioinformatics audience. It does so by introducing the fundamentals of XML, Document Type De?nitions (DTDs), XML Namespaces, XML Schema, and XML parsing, and illustrating these concepts with speci?c bioinformatics case studies. The book does not assume any previous knowledge of XML and is geared toward those who want a solid introduction to fundamental XML concepts. The book is divided into nine chapters: Chapter 1: Introduction to XML for Bioinformatics. This chapter provides an introduction to XML and describes the use of XML in biological data exchange. A bird’s-eye view of our ?rst case study, the Distributed Annotation System (DAS), is provided and we examine a sample DAS XML document. The chapter concludes with a discussion of the pros and cons of using XML in bioinformatic applications. Chapter 2: Fundamentals of XML and BSML. This chapter introduces the fundamental concepts of XML and the Bioinformatic Sequence Markup Language (BSML). We explore the origins of XML, de?ne basic rules for XML document structure, and introduce XML Na- spaces. We also explore several sample BSML documents and visualize these documents in the TM Rescentris Genomic Workspace Viewer.

Data Analytics in Bioinformatics
Data Analytics in Bioinformatics

Author: Rabinarayan Satpathy

Publisher: John Wiley & Sons

Published: 2021-01-20

Total Pages: 433

ISBN-13: 111978560X

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Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning 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. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.

XML Data Management
XML Data Management

Author: Akmal B. Chaudhri

Publisher: Addison-Wesley Professional

Published: 2003

Total Pages: 682

ISBN-13: 9780201844528

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In this book, you will find discussions on the newest native XML databases, along with information on working with XML-enabled relational database systems. In addition, XML Data Management thoroughly examines benchmarks and analysis techniques for performance of XML databases. This book is best used by students that are knowledgeable in database technology and are familiar with XML.

Bioinformatics for Geneticists
Bioinformatics for Geneticists

Author: Michael R. Barnes

Publisher: John Wiley & Sons

Published: 2003-07-01

Total Pages: 432

ISBN-13: 047086219X

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This timely book illustrates the value of bioinformatics, not simply as a set of tools but rather as a science increasingly essential to navigate and manage the host of information generated by genomics and the availability of completely sequenced genomes. Bioinformatics can be used at all stages of genetics research: to improve study design, to assist in candidate gene identification, to aid data interpretation and management and to shed light on the molecular pathology of disease-causing mutations. Written specifically for geneticists, this book explains the relevance of bioinformatics showing how it may be used to enhance genetic data mining and markedly improve genetic analysis.

Bioinformatics in Aquaculture
Bioinformatics in Aquaculture

Author: Zhanjiang (John) Liu

Publisher: John Wiley & Sons

Published: 2017-04-17

Total Pages: 605

ISBN-13: 1118782356

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Bioinformatics derives knowledge from computer analysis of biological data. In particular, genomic and transcriptomic datasets are processed, analysed and, whenever possible, associated with experimental results from various sources, to draw structural, organizational, and functional information relevant to biology. Research in bioinformatics includes method development for storage, retrieval, and analysis of the data. Bioinformatics in Aquaculture provides the most up to date reviews of next generation sequencing technologies, their applications in aquaculture, and principles and methodologies for the analysis of genomic and transcriptomic large datasets using bioinformatic methods, algorithm, and databases. The book is unique in providing guidance for the best software packages suitable for various analysis, providing detailed examples of using bioinformatic software and command lines in the context of real world experiments. This book is a vital tool for all those working in genomics, molecular biology, biochemistry and genetics related to aquaculture, and computational and biological sciences.

Machine Learning in Bioinformatics
Machine Learning in Bioinformatics

Author: Yanqing Zhang

Publisher: John Wiley & Sons

Published: 2009-02-23

Total Pages: 476

ISBN-13: 0470397411

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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.

Bioinformatics
Bioinformatics

Author: Zoé Lacroix

Publisher: Academic Press

Published: 2003-07-18

Total Pages: 466

ISBN-13: 155860829X

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The heart of the book lies in the collaboration efforts of eight distinct bioinformatics teams that describe their own unique approaches to data integration and interoperability. Each system receives its own chapter where the lead contributors provide precious insight into the specific problems being addressed by the system, why the particular architecture was chosen, and details on the system's strengths and weaknesses. In closing, the editors provide important criteria for evaluating these systems that bioinformatics professionals will find valuable. * Provides a clear overview of the state-of-the-art in data integration and interoperability in genomics, highlighting a variety of systems and giving insight into the strengths and weaknesses of their different approaches.-

XML Data Mining
XML Data Mining

Author: Andrea Tagarelli

Publisher:

Published: 2011-12-01

Total Pages: 521

ISBN-13: 9781613503584

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"This book is a collection of knowledge from experts of database, information retrieval, machine learning, and knowledge management communities in developing models, methods and systems for XML data mining that can be used to address key issues and challenges in XML data mining"--Provided by publisher.

Statistical Bioinformatics
Statistical Bioinformatics

Author: Jae K. Lee

Publisher: John Wiley & Sons

Published: 2011-09-20

Total Pages: 337

ISBN-13: 1118211529

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This book provides an essential understanding of statistical concepts necessary for the analysis of genomic and proteomic data using computational techniques. The author presents both basic and advanced topics, focusing on those that are relevant to the computational analysis of large data sets in biology. Chapters begin with a description of a statistical concept and a current example from biomedical research, followed by more detailed presentation, discussion of limitations, and problems. The book starts with an introduction to probability and statistics for genome-wide data, and moves into topics such as clustering, classification, multi-dimensional visualization, experimental design, statistical resampling, and statistical network analysis. Clearly explains the use of bioinformatics tools in life sciences research without requiring an advanced background in math/statistics Enables biomedical and life sciences researchers to successfully evaluate the validity of their results and make inferences Enables statistical and quantitative researchers to rapidly learn novel statistical concepts and techniques appropriate for large biological data analysis Carefully revisits frequently used statistical approaches and highlights their limitations in large biological data analysis Offers programming examples and datasets Includes chapter problem sets, a glossary, a list of statistical notations, and appendices with references to background mathematical and technical material Features supplementary materials, including datasets, links, and a statistical package available online Statistical Bioinformatics is an ideal textbook for students in medicine, life sciences, and bioengineering, aimed at researchers who utilize computational tools for the analysis of genomic, proteomic, and many other emerging high-throughput molecular data. It may also serve as a rapid introduction to the bioinformatics science for statistical and computational students and audiences who have not experienced such analysis tasks before.

Bioinformatics for Glycobiology and Glycomics
Bioinformatics for Glycobiology and Glycomics

Author: Claus-Wilhelm von der Lieth

Publisher: John Wiley & Sons

Published: 2009-10-01

Total Pages: 494

ISBN-13: 9780470029602

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This book is the first to be dedicated to the bioinformatics of carbohydrates and glycoproteins. It provides an introduction to this emerging field of science both for the experimentalist working in glycobiology and glycomics, and also for the computer scientist looking for background information for the development of highly sophisticated algorithmic approaches. The book provides an overview of the state-of-the-art in the field, with reviews on databases, and the tools in use for analysis, interpretation, and prediction of the structures of complex carbohydrates, and demonstrates the value of bioinformatics for glycobiology. The availability of comprehensive databases and corresponding bioinformatics tools, to access and analyse the large amounts of experimental data relating to the structure of carbohydrates, will be a prerequisite for the success of the large-scale glycomics projects that aim to decipher new, so far unknown, biological functions of glycans. Efficient bioinformatics descriptions and tools can considerably enhance the efficiency of glycomics research, in terms of data quality, analysis and experimental costs. For a complete understanding of the molecular processes in which carbohydrates are involved, such as protein–carbohydrate interactions and the impact of glycosylation on protein function, knowledge of the 3D structure of the carbohydrate, the protein–carbohydrate complex, or the glycoprotein is often indispensable. This book provides a thorough introduction into methods used for conformational analysis of carbohydrates. Key features: Describes bioinformatic approaches to handle carbohydrate-active enzymes and glycosylation. Provides an overview on bioinformatics tools that facilitate analysis of carbohydrate structures. Gives introduction into molecular modelling of carbohydrate 3D structure and carbohydrates contained in the Protein Databank. Assumes only a basic knowledge of biology and bioinformatics.