Research in Computational Molecular Biology

Research in Computational Molecular Biology

Author: Vineet Bafna

Publisher: Springer

Published: 2011-03-24

Total Pages: 595

ISBN-13: 3642200362

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This book constitutes the refereed proceedings of the 15th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2011, held in Vancouver, Canada, in March 2011. The 43 revised full papers were carefully reviewed and selected from 153 submissions. The papers cover a wide range of topics including molecular sequence analysis; recognition of genes and regulatory elements; molecular evolution; gene expression; biological networks; sequencing and genotyping technologies; genomics; population, statistical genetics; systems biology; imaging; computational proteomics; molecular structural biology.


Algorithms in Structural Molecular Biology

Algorithms in Structural Molecular Biology

Author: Bruce R. Donald

Publisher: MIT Press

Published: 2023-08-15

Total Pages: 497

ISBN-13: 0262548798

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An overview of algorithms important to computational structural biology that addresses such topics as NMR and design and analysis of proteins.Using the tools of information technology to understand the molecular machinery of the cell offers both challenges and opportunities to computational scientists. Over the past decade, novel algorithms have been developed both for analyzing biological data and for synthetic biology problems such as protein engineering. This book explains the algorithmic foundations and computational approaches underlying areas of structural biology including NMR (nuclear magnetic resonance); X-ray crystallography; and the design and analysis of proteins, peptides, and small molecules. Each chapter offers a concise overview of important concepts, focusing on a key topic in the field. Four chapters offer a short course in algorithmic and computational issues related to NMR structural biology, giving the reader a useful toolkit with which to approach the fascinating yet thorny computational problems in this area. A recurrent theme is understanding the interplay between biophysical experiments and computational algorithms. The text emphasizes the mathematical foundations of structural biology while maintaining a balance between algorithms and a nuanced understanding of experimental data. Three emerging areas, particularly fertile ground for research students, are highlighted: NMR methodology, design of proteins and other molecules, and the modeling of protein flexibility. The next generation of computational structural biologists will need training in geometric algorithms, provably good approximation algorithms, scientific computation, and an array of techniques for handling noise and uncertainty in combinatorial geometry and computational biophysics. This book is an essential guide for young scientists on their way to research success in this exciting field.


Algorithms in Computational Molecular Biology

Algorithms in Computational Molecular Biology

Author: Mourad Elloumi

Publisher: John Wiley & Sons

Published: 2011-04-04

Total Pages: 1027

ISBN-13: 1118101987

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This book represents the most comprehensive and up-to-date collection of information on the topic of computational molecular biology. Bringing the most recent research into the forefront of discussion, Algorithms in Computational Molecular Biology studies the most important and useful algorithms currently being used in the field, and provides related problems. It also succeeds where other titles have failed, in offering a wide range of information from the introductory fundamentals right up to the latest, most advanced levels of study.


Learning and Inference in Computational Systems Biology

Learning and Inference in Computational Systems Biology

Author: Neil D. Lawrence

Publisher:

Published: 2010

Total Pages: 384

ISBN-13:

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Tools and techniques for biological inference problems at scales ranging from genome-wide to pathway-specific. Computational systems biology unifies the mechanistic approach of systems biology with the data-driven approach of computational biology. Computational systems biology aims to develop algorithms that uncover the structure and parameterization of the underlying mechanistic model--in other words, to answer specific questions about the underlying mechanisms of a biological system--in a process that can be thought of as learning or inference. This volume offers state-of-the-art perspectives from computational biology, statistics, modeling, and machine learning on new methodologies for learning and inference in biological networks.The chapters offer practical approaches to biological inference problems ranging from genome-wide inference of genetic regulation to pathway-specific studies. Both deterministic models (based on ordinary differential equations) and stochastic models (which anticipate the increasing availability of data from small populations of cells) are considered. Several chapters emphasize Bayesian inference, so the editors have included an introduction to the philosophy of the Bayesian approach and an overview of current work on Bayesian inference. Taken together, the methods discussed by the experts in Learning and Inference in Computational Systems Biology provide a foundation upon which the next decade of research in systems biology can be built. Florence d'Alch e-Buc, John Angus, Matthew J. Beal, Nicholas Brunel, Ben Calderhead, Pei Gao, Mark Girolami, Andrew Golightly, Dirk Husmeier, Johannes Jaeger, Neil D. Lawrence, Juan Li, Kuang Lin, Pedro Mendes, Nicholas A. M. Monk, Eric Mjolsness, Manfred Opper, Claudia Rangel, Magnus Rattray, Andreas Ruttor, Guido Sanguinetti, Michalis Titsias, Vladislav Vyshemirsky, David L. Wild, Darren Wilkinson, Guy Yosiphon


Computational Molecular Biology

Computational Molecular Biology

Author: Peter Clote

Publisher: Wiley

Published: 2000-10-03

Total Pages: 304

ISBN-13: 9780471872528

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Recently molecular biology has undergone unprecedented developmentgenerating vast quantities of data needing sophisticatedcomputational methods for analysis, processing and archiving. Thisrequirement has given birth to the truly interdisciplinary field ofcomputational biology, or bioinformatics, a subject reliant on boththeoretical and practical contributions from statistics,mathematics, computer science and biology. * Provides the background mathematics required to understand whycertain algorithms work * Guides the reader through probability theory, entropy andcombinatorial optimization * In-depth coverage of molecular biology and protein structureprediction * Includes several less familiar algorithms such as DNAsegmentation, quartet puzzling and DNA strand separationprediction * Includes class tested exercises useful for self-study * Source code of programs available on a Web site Primarily aimed at advanced undergraduate and graduate studentsfrom bioinformatics, computer science, statistics, mathematics andthe biological sciences, this text will also interest researchersfrom these fields.


Handbook of Research on Computational and Systems Biology

Handbook of Research on Computational and Systems Biology

Author: Limin Angela Liu

Publisher: IGI Global

Published: 2011

Total Pages: 0

ISBN-13: 9781609604912

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"This book offers information on the state-of-the-art development in the fields of computational biology and systems biology, presenting methods, tools, and applications of these fields by many leading experts around the globe"--Provided by publisher.


Introduction to Computational Biology

Introduction to Computational Biology

Author: Michael S. Waterman

Publisher: CRC Press

Published: 2018-05-02

Total Pages: 456

ISBN-13: 1351437089

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Biology is in the midst of a era yielding many significant discoveries and promising many more. Unique to this era is the exponential growth in the size of information-packed databases. Inspired by a pressing need to analyze that data, Introduction to Computational Biology explores a new area of expertise that emerged from this fertile field- the combination of biological and information sciences. This introduction describes the mathematical structure of biological data, especially from sequences and chromosomes. After a brief survey of molecular biology, it studies restriction maps of DNA, rough landmark maps of the underlying sequences, and clones and clone maps. It examines problems associated with reading DNA sequences and comparing sequences to finding common patterns. The author then considers that statistics of pattern counts in sequences, RNA secondary structure, and the inference of evolutionary history of related sequences. Introduction to Computational Biology exposes the reader to the fascinating structure of biological data and explains how to treat related combinatorial and statistical problems. Written to describe mathematical formulation and development, this book helps set the stage for even more, truly interdisciplinary work in biology.


Statistical Modeling and Machine Learning for Molecular Biology

Statistical Modeling and Machine Learning for Molecular Biology

Author: Alan Moses

Publisher: CRC Press

Published: 2017-01-06

Total Pages: 281

ISBN-13: 1482258609

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• Assumes no background in statistics or computers • Covers most major types of molecular biological data • Covers the statistical and machine learning concepts of most practical utility (P-values, clustering, regression, regularization and classification) • Intended for graduate students beginning careers in molecular biology, systems biology, bioengineering and genetics


Research in Computational Molecular Biology

Research in Computational Molecular Biology

Author: Terence Terry Speed

Publisher: Springer Science & Business Media

Published: 2007-04-05

Total Pages: 565

ISBN-13: 3540716807

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This book constitutes the refereed proceedings of the 11th Annual International Conference on Research in Computational Molecular Biology, RECOMB 2007, held in Oakland, CA, USA in April 2007. The 37 revised full papers address all current issues in algorithmic, theoretical, and experimental bioinformatics.