bioinformatics sequence alignment and markov models

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Bioinformatics Sequence Alignment And Markov Models

Author : Kal Sharma
ISBN : 9780071593076
Genre : Technology & Engineering
File Size : 71. 43 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


Author : Kal Renganathan Sharma
ISBN : 0071635823
Genre : Bioinformatics
File Size : 77. 2 MB
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Hidden Markov Models For Bioinformatics

Author : T. Koski
ISBN : 1402001355
Genre : Computers
File Size : 36. 81 MB
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Hidden Markov Models for Bioinformatics

Biological Sequence Analysis

Author : Richard Durbin
ISBN : 9781139457392
Genre : Science
File Size : 57. 64 MB
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Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.


Author : Source Wikipedia
ISBN : 1230629610
Genre :
File Size : 58. 11 MB
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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, ..

Problems And Solutions In Biological Sequence Analysis

Author : Mark Borodovsky
ISBN : 9781139458122
Genre : Science
File Size : 20. 5 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.

Protein Homology Detection Through Alignment Of Markov Random Fields

Author : Jinbo Xu
ISBN : 9783319149141
Genre : Computers
File Size : 48. 91 MB
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This work covers sequence-based protein homology detection, a fundamental and challenging bioinformatics problem with a variety of real-world applications. The text first surveys a few popular homology detection methods, such as Position-Specific Scoring Matrix (PSSM) and Hidden Markov Model (HMM) based methods, and then describes a novel Markov Random Fields (MRF) based method developed by the authors. MRF-based methods are much more sensitive than HMM- and PSSM-based methods for remote homolog detection and fold recognition, as MRFs can model long-range residue-residue interaction. The text also describes the installation, usage and result interpretation of programs implementing the MRF-based method.

Understanding Bioinformatics

Author : Marketa Zvelebil
ISBN : 9781136976964
Genre : Medical
File Size : 68. 77 MB
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Suitable for advanced undergraduates and postgraduates, Understanding Bioinformatics provides a definitive guide to this vibrant and evolving discipline. The book takes a conceptual approach._It guides the reader from first principles through to an understanding of the computational techniques and the key algorithms._ Understanding Bioinformatics is an invaluable companion for students from their first encounter with the subject through to more advanced studies._The book is divided into seven parts, with the opening_part introducing the basics of nucleic acids, proteins and databases._Subsequent parts are divided into 'Applications' and 'Theory' Chapters, allowing readers to focus their attention effectively._ In each section, the Applications Chapter provides a fast and straightforward route to understanding the main concepts and 'getting started'. Each of these is then followed by Theory Chapters which give greater detail and present the underlying mathematics. In Part 2, Sequence Alignments, the Applications Chapter shows the reader how to get started on producing and analyzing sequence alignments, and using sequences for database searching, while the next two chapters look closely at the more advanced techniques and the mathematical algorithms involved._Part 3_covers evolutionary processes and shows how bioinformatics can be used to help build phylogenetic trees._Part 4_looks at the characteristics of whole genomes. In_Parts 5_and_6_the focus turns to secondary and tertiary structure _ predicting structural conformation and analyzing structure-function relationships._The last_part surveys methods of analyzing data from a set of genes or proteins of an organism and is rounded off with an overview of systems biology. The writing style of Understanding Bioinformatics is notable for its clarity, while the extensive, full-color artwork has been designed to present the key concepts with simplicity and consistency._Each chapter uses mind-maps and flow diagrams to give an overview of the conceptual links within each topic._

Introduction To Mathematical Methods In Bioinformatics

Author : Alexander Isaev
ISBN : 3540219730
Genre : Science
File Size : 51. 45 MB
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This book looks at the mathematical foundations of the models currently in use. All existing books on bioinformatics are software-orientated and they concentrate on computer implementations of mathematical models of biology. This book is unique in the sense that it looks at the mathematical foundations of the models, which are crucial for correct interpretation of the outputs of the models.

Handbook Of Hidden Markov Models In Bioinformatics

Author : Martin Gollery
ISBN : 9781420011807
Genre : Science
File Size : 83. 12 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.

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