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Bioinformatics Sequence Alignment And Markov Models

Author: Kal Sharma
Publisher: McGraw Hill Professional
ISBN: 0071593071
Size: 34.92 MB
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GET FULLY UP-TO-DATE ON BIOINFORMATICS-THE TECHNOLOGY OF THE 21ST CENTURY Bioinformatics showcases the latest developments in the field along with all the foundational information you'll need. It provides in-depth coverage of a wide range of autoimmune disorders and detailed analyses of suffix trees, plus late-breaking advances regarding biochips and genomes. Featuring helpful gene-finding algorithms, Bioinformatics offers key information on sequence alignment, HMMs, HMM applications, protein secondary structure, microarray techniques, and drug discovery and development. Helpful diagrams accompany mathematical equations throughout, and exercises appear at the end of each chapter to facilitate self-evaluation. This thorough, up-to-date resource features: Worked-out problems illustrating concepts and models End-of-chapter exercises for self-evaluation Material based on student feedback Illustrations that clarify difficult math problems A list of bioinformatics-related websites Bioinformatics covers: Sequence representation and alignment Hidden Markov models Applications of HMMs Gene finding Protein secondary structure prediction Microarray techniques Drug discovery and development Internet resources and public domain databases

Problems And Solutions In Biological Sequence Analysis

Author: Mark Borodovsky
Publisher: Cambridge University Press
ISBN: 1139458124
Size: 73.45 MB
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This book is the first of its kind to provide a large collection of bioinformatics problems with accompanying solutions. Notably, the problem set includes all of the problems offered in Biological Sequence Analysis (BSA), by Durbin et al., widely adopted as a required text for bioinformatics courses at leading universities worldwide. Although many of the problems included in BSA as exercises for its readers have been repeatedly used for homework and tests, no detailed solutions for the problems were available. Bioinformatics instructors had therefore frequently expressed a need for fully worked solutions and a larger set of problems for use on courses. This book provides just that: following the same structure as BSA and significantly extending the set of workable problems, it will facilitate a better understanding of the contents of the chapters in BSA and will help its readers develop problem-solving skills that are vitally important for conducting successful research in the growing field of bioinformatics. All of the material has been class-tested by the authors at Georgia Tech, where the first ever M.Sc. degree program in Bioinformatics was held.

Handbook Of Hidden Markov Models In Bioinformatics

Author: Martin Gollery
Publisher: CRC Press
ISBN: 1420011804
Size: 17.72 MB
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Demonstrating that many useful resources, such as databases, can benefit most bioinformatics projects, the Handbook of Hidden Markov Models in Bioinformatics focuses on how to choose and use various methods and programs available for hidden Markov models (HMMs). The book begins with discussions on key HMM and related profile methods, including the HMMER package, the sequence analysis method (SAM), and the PSI-BLAST algorithm. It then provides detailed information about various types of publicly available HMM databases, such as Pfam, PANTHER, COG, and metaSHARK. After outlining ways to develop and use an automated bioinformatics workflow, the author describes how to make custom HMM databases using HMMER, SAM, and PSI-BLAST. He also helps you select the right program to speed up searches. The final chapter explores several applications of HMM methods, including predictions of subcellular localization, posttranslational modification, and binding site. By learning how to effectively use the databases and methods presented in this handbook, you will be able to efficiently identify features of biological interest in your data.

Probabilistic Modeling In Bioinformatics And Medical Informatics

Author: Dirk Husmeier
Publisher: Springer Science & Business Media
ISBN: 1846281199
Size: 72.19 MB
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Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.

Understanding Bioinformatics

Author: Marketa J. Zvelebil
Publisher: Garland Science
ISBN: 9780815340249
Size: 39.39 MB
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Suitable for advanced undergraduates & postgraduates, this book provides a definitive guide to bioinformatics. It takes a conceptual approach & guides the reader from first principles through to an understanding of the computational techniques & the key algorithms.

Bioinformatics

Author: Source Wikipedia
Publisher: University-Press.org
ISBN: 9781230629612
Size: 30.52 MB
Format: PDF, Kindle
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 294. Chapters: Proteomics, Hidden Markov model, Biostatistics, Proteome, Sequence alignment, Full genome sequencing, [email protected], Metagenomics, Mass-spectrometry software, DNA microarray, Protein structure prediction, Homology modeling, Synthetic biology, Metabolomics, DNA barcoding, Multiple sequence alignment, Haplogroup M, Systems biology, Protein-protein interaction prediction, Flux balance analysis, Models of DNA evolution, Macromolecular docking, CaBIG, Metabolic network modelling, Substitution model, ChIP-on-chip, DNA binding site, Morphometrics, Personal genomics, Computational Resource for Drug Discovery, Biochip, Complex system biology, DNA sequencing theory, Sequence motif, UniProt, Protein subcellular localization prediction, Ionomics, Biopunk, 1000 Genomes Project, Statistical potential, Sequence assembly, Gene Ontology, List of biological databases, Demographic and Health Surveys, Gene prediction, Modelling biological systems, Shamkant Navathe, UCSC Genome Browser, Bioconductor, Precision and recall, Molecular modelling, Sequence profiling tool, FASTA format, Threading, Biclustering, John Quackenbush, Biomedical text mining, FASTQ format, Substitution matrix, Stockholm format, Interactome, CASP, Sensitivity and specificity, High-throughput screening, MicroRNA and microRNA target database, Scoring functions for docking, Minimum Information Standards, Genenetwork, Stochastic context-free grammar, Multiple displacement amplification, Virtual screening, List of phylogenetics software, Protein fragment library, World Health Imaging, Telemedicine, and Informatics Alliance, List of MeSH codes, Automated species identification, Ontology engineering, Gene nomenclature, Statistical coupling analysis, Suspension array technology, Sulston score, NeuroLex, Society for Mathematical Biology, Bayesian inference in phylogeny, ..

Machine Learning Based Sequence Analysis Bioinformatics And Nanopore Transduction Detection

Author: Stephen Winters-Hilt
Publisher: Lulu.com
ISBN: 1257645250
Size: 68.89 MB
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This is intended to be a simple and accessible book on machine learning methods and their application in computational genomics and nanopore transduction detection. This book has arisen from eight years of teaching one-semester courses on various machine-learning, cheminformatics, and bioinformatics topics. The book begins with a description of ad hoc signal acquisition methods and how to orient on signal processing problems with the standard tools from information theory and signal analysis. A general stochastic sequential analysis (SSA) signal processing architecture is then described that implements Hidden Markov Model (HMM) methods. Methods are then shown for classification and clustering using generalized Support Vector Machines, for use with the SSA Protocol, or independent of that approach. Optimization metaheuristics are used for tuning over algorithmic parameters throughout. Hardware implementations and short code examples of the various methods are also described.

Bioinformatics

Author: Venkatarajan Mathura
Publisher: Springer Science & Business Media
ISBN: 0387848703
Size: 65.16 MB
Format: PDF, Mobi
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Bioinformatics is an evolving field that is gaining popularity due to genomics, proteomics and other high-throughput biological methods. The function of bioinformatic scientists includes biological data storage, retrieval and in silico analysis of the results from large-scale experiments. This requires a grasp of knowledge mining algorithms, a thorough understanding of biological knowledge base, and the logical relationship of entities that describe a process or the system. Bioinformatics researchers are required to be trained in multidisciplinary fields of biology, mathematics and computer science. Currently the requirements are satisfied by ad hoc researchers who have specific skills in biology or mathematics/computer science. But the learning curve is steep and the time required to communicate using domain specific terms is becoming a major bottle neck in scientific productivity. This workbook provides hands-on experience which has been lacking for qualified bioinformatics researchers.

Bioinformatics

Author: Bertil Schmidt
Publisher: CRC Press
ISBN: 1439858365
Size: 59.76 MB
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New sequencing technologies have broken many experimental barriers to genome scale sequencing, leading to the extraction of huge quantities of sequence data. This expansion of biological databases established the need for new ways to harness and apply the astounding amount of available genomic information and convert it into substantive biological understanding. A complilation of recent approaches from prominent researchers, Bioinformatics: High Performance Parallel Computer Architectures discusses how to take advantage of bioinformatics applications and algorithms on a variety of modern parallel architectures. Two factors continue to drive the increasing use of modern parallel computer architectures to address problems in computational biology and bioinformatics: high-throughput techniques for DNA sequencing and gene expression analysis—which have led to an exponential growth in the amount of digital biological data—and the multi- and many-core revolution within computer architecture. Presenting key information about how to make optimal use of parallel architectures, this book: Describes algorithms and tools including pairwise sequence alignment, multiple sequence alignment, BLAST, motif finding, pattern matching, sequence assembly, hidden Markov models, proteomics, and evolutionary tree reconstruction Addresses GPGPU technology and the associated massively threaded CUDA programming model Reviews FPGA architecture and programming Presents several parallel algorithms for computing alignments on the Cell/BE architecture, including linear-space pairwise alignment, syntenic alignment, and spliced alignment Assesses underlying concepts and advances in orchestrating the phylogenetic likelihood function on parallel computer architectures (ranging from FPGAs upto the IBM BlueGene/L supercomputer) Covers several effective techniques to fully exploit the computing capability of many-core CUDA-enabled GPUs to accelerate protein sequence database searching, multiple sequence alignment, and motif finding Explains a parallel CUDA-based method for correcting sequencing base-pair errors in HTSR data Because the amount of publicly available sequence data is growing faster than single processor core performance speed, modern bioinformatics tools need to take advantage of parallel computer architectures. Now that the era of the many-core processor has begun, it is expected that future mainstream processors will be parallel systems. Beneficial to anyone actively involved in research and applications, this book helps you to get the most out of these tools and create optimal HPC solutions for bioinformatics.