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Introduction to Computational Biology,Maps, Sequences and Genomes
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.
by Michael S. Waterman
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Kernel Methods in Computational Biology,
A detailed overview of current research in kernel methods and their application to computational biology.
by Bernhard Schölkopf
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Algebraic Statistics for Computational Biology,
This book, first published in 2005, offers an introduction to the application of algebraic statistics to computational biology.
by L. Pachter
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Computational Biology,Unix/Linux, data processing and programming : with 19 figures and 12 tables
This book is a practical introduction to Unix/Linux and programming for biologists as well as for chemists and physicists who work in bioinformatics and biophysics. The goal is to learn about the power of the stream editor 'sed' and the programming languages 'awk' and 'perl' in order to extract or format information from various sources. It is written for beginners with no computational knowledge. Basic programming constructs are introduced and applied. With this book, the reader will be able to work in the Unix environment (BSD, Linux, Knoppix, MacOSX, CygWin) and to write programs in order to format and analyse large data files.
by Röbbe Wünschiers
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Introduction to Computational Biology,An Evolutionary Approach
Written with the advanced undergraduate in mind, this book introduces into the field of Bioinformatics. The authors explain the computational and conceptional background to the analysis of large-scale sequence data. Many of the corresponding analysis methods are rooted in evolutionary thinking, which serves as a common thread throughout the book. The focus is on methods of comparative genomics and subjects covered include: alignments, gene finding, phylogeny, and the analysis of single nucleotide polymorphisms (SNPs). The volume contains exercises, questions & answers to selected problems.
by Bernhard Haubold
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Computational biology,hearing before the Subcommittee on Science, Technology, and Space of the Committee on Commerce, Science, and Transportation, United States Senate, One Hundred Fourth Congress, second session, September 17, 1996
by United States. Congress. Senate. Committee on Commerce, Science, and Transportation. Subcommittee on Science, Technology, and Space
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Compact Handbook of Computational Biology,
The assimilation of computational methods into the life sciences has played an important role in advancing biological research. From sequencing genomes to discovering motifs in large collections of functionally equivalent sequences of nucleic acids and proteins, the value of powerful computational tools has become abundantly clear. The Compact Handbook of Computational Biology describes the foundational concepts, techniques, and challenges of biomolecular sequence analysis, biopolymer structure analysis and prediction, genomics, and molecular evolution. Comprising contributions from renowned scientists, many of whom pioneered current methodologies, the Compact Handbook of Computational Biology offers interdisciplinary and authoritative coverage of methods for analysis of nucleic acid and protein sequences and structures, definition and detection of motifs in nucleic acid and protein sequences, protein structure prediction, and discrete modeling of biopolymers. Specific computational topics include computer-assisted research on protein folding, computer-assisted studies of DNA-protein interactions, computer-assisted genomics, proteomics, and comparative genomics, and computer-assisted studies of genome evolution at the molecular (DNA) level. Offering a survey of specific algorithms that have proven successful in molecular biology, genomics, structural biology, and bioinformatics, this invaluable handbook is an authoritative source covering terminology, software, and applications of computational biology.
by A.K. Konopka
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Computational Biology,A Statistical Mechanics Perspective
Quantitative methods have a particular knack for improving any field they touch. For biology, computational techniques have led to enormous strides in our understanding of biological systems, but there is still vast territory to cover. Statistical physics especially holds great potential for elucidating the structural-functional relationships in biomolecules, as well as their static and dynamic properties. Breaking New Ground Computational Biology: A Statistical Mechanics Perspective is the first book dedicated to the interface between statistical physics and bioinformatics. Introducing both equilibrium and nonequilibrium statistical mechanics in a manner tailored to computational biologists, the author applies these methods to understand and model the properties of various biomolecules and biological networks at the systems level. Unique Vision, Novel Approach Blossey combines his enthusiasm for uniting the fields of physics and computational biology with his considerable experience, knowledge, and gift for teaching. He uses numerous examples and tasks to illustrate and test understanding of the concepts, and he supplies a detailed keyword list for easy navigation and comprehension. His approach takes full advantage of the latest tools in statistical physics and computer science to build a strong set of tools for confronting new challenges in computational biology. Making the concepts crystal clear without sacrificing mathematical rigor, Computational Biology: A Statistical Mechanics Perspective is the perfect tool to broaden your skills in computational biology.
by Ralf Blossey
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Computational Cell Biology,
This textbook provides an introduction to dynamic modeling in molecular cell biology, taking a computational and intuitive approach. Detailed illustrations, examples, and exercises are included throughout the text. Appendices containing mathematical and computational techniques are provided as a reference tool.
by Christopher P. Fall
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An Introduction to Systems Biology,Design Principles of Biological Circuits
Thorough and accessible, this book presents the design principles of biological systems, and highlights the recurring circuit elements that make up biological networks. It provides a simple mathematical framework which can be used to understand and even design biological circuits. The textavoids specialist terms, focusing instead on several well-studied biological systems that concisely demonstrate key principles. An Introduction to Systems Biology: Design Principles of Biological Circuits builds a solid foundation for the intuitive understanding of general principles. It encourages the reader to ask why a system is designed in a particular way and then proceeds to answer with simplified models.
by Uri Alon
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Advances in Computational Biology,
Proceedings of The 2009 International Conference on Bioinformatics and Computational Biology in Las Vegas, NV, July 13-16, 2009. Recent advances in Computational Biology are covered through a variety of topics. Both inward research (core areas of computational biology and computer science) and outward research (multi-disciplinary, Inter-disciplinary, and applications) will be covered during the conferences. These include: Gene regulation, Gene expression databases, Gene pattern discovery and identification, Genetic network modeling and inference, Gene expression analysis, RNA and DNA structure and sequencing, Biomedical engineering, Microarrays, Molecular sequence and structure databases, Molecular dynamics and simulation, Molecular sequence classification, alignment and assembly, Image processing In medicine and biological sciences, Sequence analysis and alignment, Informatics and Statistics in Biopharmaceutical Research, Software tools for computational biology and bioinformatics, Comparative genomics; and more.
by Hamid R. Arabnia
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Computational Molecular Biology,
This volume contains papers demonstrating the variety and richness of computational problems motivated by molecular biology. The application areas within biology that give rise to the problems studied in these papers include solid molecular modeling, sequence comparison, phylogeny, evolution, mapping, DNA chips, protein folding and 2D gel technology. The mathematical techniques used are algorithmics, combinatorics, optimization, probability, graph theory, complexity and applied mathematics. This is the fourth volume in the Discrete Applied Mathematics series on computational molecular biology, which is devoted to combinatorial and algorithmic techniques in computational molecular biology. This series publishes novel research results on the mathematical and algorithmic foundations of the inherently discrete aspects of computational biology. Key features: • protein folding • phylogenetic inference • 2-dimensional gel analysis • graphical models for sequencing by hybridisation • dynamic visualization of molecular surfaces • problems and algorithms in sequence alignment This book is a reprint of Discrete Applied Mathematics Volume 127, Number 1.
by Sorin Istrail
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Computational Biology of Cancer,Lecture Notes and Mathematical Modeling
The book shows how mathematical and computational models can be used to study cancer biology. It introduces the concept of mathematical modeling and then applies it to a variety of topics in cancer biology. These include aspects of cancer initiation and progression, such as the somatic evolution of cells, genetic instability, and angiogenesis. The book also discusses the use of mathematical models for the analysis of therapeutic approaches such as chemotherapy, immunotherapy, and the use of oncolytic viruses.
by Dominik Wodarz
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Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R,
Emphasizing the search for patterns within and between biological sequences, trees, and graphs, Combinatorial Pattern Matching Algorithms in Computational Biology Using Perl and R shows how combinatorial pattern matching algorithms can solve computational biology problems that arise in the analysis of genomic, transcriptomic, proteomic, metabolomic, and interactomic data. It implements the algorithms in Perl and R, two widely used scripting languages in computational biology. The book provides a well-rounded explanation of traditional issues as well as an up-to-date account of more recent developments, such as graph similarity and search. It is organized around the specific algorithmic problems that arise when dealing with structures that are commonly found in computational biology, including biological sequences, trees, and graphs. For each of these structures, the author makes a clear distinction between problems that arise in the analysis of one structure and in the comparative analysis of two or more structures. He also presents phylogenetic trees and networks as examples of trees and graphs in computational biology. This book supplies a comprehensive view of the whole field of combinatorial pattern matching from a computational biology perspective. Along with thorough discussions of each biological problem, it includes detailed algorithmic solutions in pseudo-code, full Perl and R implementation, and pointers to other software, such as those on CPAN and CRAN.
by Gabriel Valiente
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Computational Biology,Issues and Applications in Oncology
This volume covers techniques in computational biology and their applications in oncology. It details advanced statistical methods, heuristic algorithms, cluster analysis, data modeling, and image and pattern analysis applied to cancer research.
by Tuan Pham
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