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Computer Science 2006 - Computational Biology
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Jin Xiong
Essential Bioinformatics is a concise yet comprehensive textbook of bioinformatics, which provides a broad introduction to the entire field. Written specifically for a life science audience, the basics of bioinformatics are explained, followed by discussions of the state-of-the-art computational tools available to solve biological research problems. All key areas of bioinformatics are covered including biological databases, sequence alignment, genes and promoter prediction, molecular phylogenetics, structural bioinformatics, genomics and proteomics. The book emphasizes how computational methods work and compares the strengths and weaknesses of different methods. This balanced yet easily accessible text will be invaluable to students who do not have sophisticated computational backgrounds. Technical details of computational algorithms are explained with a minimum use of mathematical formulae; graphical illustrations are used in their place to aid understanding. The effective synthesis of existing literature as well as in-depth and up-to-date coverage of all key topics in bioinformatics make this an ideal textbook for all bioinformatics courses taken by life science students and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research.
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Bernhard O. Palsson
Genome sequences are now available that enable us to determine the biological components that make up a cell or an organism. The new discipline of systems biology examines how these components interact and form networks, and how the networks generate whole cell functions corresponding to observable phenotypes. This textbook describes how to model networks, determine their properties, and relate these to phenotypic functions. Some knowledge of linear algebra and biochemistry is required, since the book reflects the irreversible trend of increasing mathematical content in biology education.
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Dov Stekel
DNA microarrays have revolutionized molecular biology and are becoming a standard tool in the field. Dov Stekel's book is a comprehensive guide to the mathematics, statistics, and computing required to use microarrays successfully. Unlike traditional molecular biology, the successful use of DNA microarrays requires the application of statistics and computing to design the arrays and experiments, and to analyze and manage the data. This book is written for researchers, clinicians, and laboratory managers.
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Dan Gusfield
Traditionally an area of study in computer science, string algorithms have, in recent years, become an increasingly important part of biology, particularly genetics. This volume is a comprehensive look at computer algorithms for string processing. In addition to pure computer science, Gusfield adds extensive discussions on biological problems that are cast as string problems and on methods developed to solve them. This text emphasizes the fundamental ideas and techniques central to today's applications. New approaches to this complex material simplify methods that up to now have been for the specialist alone. With over 400 exercises to reinforce the material and develop additional topics, the book is suitable as a text for graduate or advanced undergraduate students in computer science, computational biology, or bio-informatics.
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Richard Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison
Probablistic models are becoming increasingly important in analyzing 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 analyzing 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 is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field.
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Pierre Baldi, G. Wesley Hatfield
Massive data acquisition technologies--such as genome sequencing, high-throughput drug screening, and DNA arrays--are in the process of revolutionizing biology and medicine. This concise, user-friendly and interdisciplinary guide to DNA microarray technology is an introduction and a reference for both biologists and computational scientists. The authors describe the underlying technologies and offer an awareness of the "noise" and pitfalls present in the data generated. They also provide an idea of the different data mining techniques and algorithms that are available to interpret data, and the advantages and disadvantages of each in differing situations.
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Rex A. Dwyer
In this introduction to computational molecular biology, Rex Dwyer explains many basic computational problems and gives concise, working programs to solve them in the Perl programming language. With minimal prerequisites, he covers the biological background for each problem, develops a model for the solution, and then introduces the Perl concepts needed to implement the solution. The chapters discuss pairwise and multiple sequence alignment, fast database searches for homologous sequences, protein motif identification, genome rearrangement, physical mapping, phylogeny reconstruction, satellite identification, sequence assembly, gene finding, and RNA secondary structure. Concrete examples and a step-by-step approach enable readers to grasp the computational and statistical methods.
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Edited by Marco Salemi, Anne-Mieke Vandamme
The Phylogenetic Handbook is a broad introduction to the theory and practice of nucleotide and amino acid phylogenetic analysis. As an unique feature of this book, each chapter contains an extensive practical section, in which step-by-step exercises on real data sets introduce the most widely used phylogeny software including CLUSTAL, PHYLIP, PAUP*, DAMBE, TREE-PUZZLE, TREECON, SplitsTree, TreeView, SimPlot, MEGA2, PAML and BOOTSCANNING. The book provides a strong background in basic topics: the use of sequence databases, alignment algorithms, tree-building methods, estimation of genetic distances, and testing models of evolution.
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Gonzalo Navarro, Mathieu Raffinot
Recent years have witnessed a dramatic increase of interest in sophisticated string matching problems, especially in information retrieval and computational biology. This book presents a practical approach to string matching problems, focusing on the algorithms and implementations that perform best in practice. It covers searching for simple, multiple and extended strings, as well as regular expressions, and exact and approximate searching. It includes all the most significant new developments in complex pattern searching. The clear explanations, step-by-step examples, algorithm pseudocode, and implementation efficiency maps will enable researchers, professionals and students in bioinformatics, computer science, and software engineering to choose the most appropriate algorithms for their applications.
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S. Robin, F. Rodolphe, S. Schbath
An important problem in computational biology is identifying short DNA sequences (mathematically, 'words') associated to a biological function. One approach consists in determining whether a particular word is simply random or is of statistical significance, for example, because of its frequency or location. This book introduces the mathematical and statistical ideas used in solving this so-called exceptional word problem. It begins with a detailed description of the principal models used in sequence analysis: Markovian models are central here and capture compositional information on the sequence being analysed. There follows an introduction to several statistical methods that are used for finding exceptional words with respect to the model used. The second half of the book is illustrated with numerous examples provided from the analysis of bacterial genomes, making this a practical guide for users facing a real situation and needing to make an adequate procedure choice.
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Edited by L. Pachter, B. Sturmfels
The quantitative analysis of biological sequence data is based on methods from statistics coupled with efficient algorithms from computer science. Algebra provides a framework for unifying many of the seemingly disparate techniques used by computational biologists. This book offers an introduction to this mathematical framework and describes tools from computational algebra for designing new algorithms for exact, accurate results. These algorithms can be applied to biological problems such as aligning genomes, finding genes and constructing phylogenies. As the first book in the exciting and dynamic area, it will be welcomed as a text for self-study or for advanced undergraduate and beginning graduate courses.
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Wulfram Gerstner, Werner M. Kistler
This introduction to spiking neurons can be used in advanced-level courses in computational neuroscience, theoretical biology, neural modeling, biophysics, or neural networks. It focuses on phenomenological approaches rather than detailed models in order to provide the reader with a conceptual framework. The authors formulate the theoretical concepts clearly without many mathematical details. While the book contains standard material for courses in computational neuroscience, neural modeling, or neural networks, it also provides an entry to current research. No prior knowledge beyond undergraduate mathematics is required.
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