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| introduction to computational molecular biology: Introduction to Computational Molecular Biology João Carlos Setubal, João Meidanis, 1997 Basic concepts of molecular biology. Strings, graphs, and algorithms. Sequence comparasion and database search. Fragment assembly of DNA. Physical mapping of DNA. Phylogenetic trees. Genome rearrangements. Molecular structure prediction. epilogue: computing with DNA. Answers to selected exercises. References. index. |
| introduction to computational molecular biology: Introduction to Computational Biology Michael S. Waterman, 2018-05-02 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. |
| introduction to computational molecular biology: Handbook of Computational Molecular Biology Srinivas Aluru, 2005-12-21 The enormous complexity of biological systems at the molecular level must be answered with powerful computational methods. Computational biology is a young field, but has seen rapid growth and advancement over the past few decades. Surveying the progress made in this multidisciplinary field, the Handbook of Computational Molecular Biology of |
| introduction to computational molecular biology: Computational Molecular Biology Peter Clote, Rolf Backofen, 2000-10-03 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. |
| introduction to computational molecular biology: An Introduction to Bioinformatics Algorithms Neil C. Jones, Pavel A. Pevzner, 2004-08-06 An introductory text that emphasizes the underlying algorithmic ideas that are driving advances in bioinformatics. This introductory text offers a clear exposition of the algorithmic principles driving advances in bioinformatics. Accessible to students in both biology and computer science, it strikes a unique balance between rigorous mathematics and practical techniques, emphasizing the ideas underlying algorithms rather than offering a collection of apparently unrelated problems. The book introduces biological and algorithmic ideas together, linking issues in computer science to biology and thus capturing the interest of students in both subjects. It demonstrates that relatively few design techniques can be used to solve a large number of practical problems in biology, and presents this material intuitively. An Introduction to Bioinformatics Algorithms is one of the first books on bioinformatics that can be used by students at an undergraduate level. It includes a dual table of contents, organized by algorithmic idea and biological idea; discussions of biologically relevant problems, including a detailed problem formulation and one or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a glimpse of the inspirations and motivations for real work in bioinformatics, making the concepts presented in the text more concrete and the techniques more approachable.PowerPoint presentations, practical bioinformatics problems, sample code, diagrams, demonstrations, and other materials can be found at the Author's website. |
| introduction to computational molecular biology: Kernel Methods in Computational Biology Bernhard Schölkopf, Koji Tsuda, Jean-Philippe Vert, 2004 A detailed overview of current research in kernel methods and their application to computational biology. |
| introduction to computational molecular biology: Computational Cell Biology Christopher P. Fall, Eric S. Marland, John M. Wagner, John J. Tyson, 2007-06-04 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. |
| introduction to computational molecular biology: Introduction to Computational Molecular Biology João Carlos Setubal, João Meidanis, 1997 |
| introduction to computational molecular biology: Quickstart Molecular Biology Philip N. Benfey, 2014 This book is an introductory course in molecular biology for mathematicians, physicists, and engineers. It covers the basic features of DNA, proteins, and cells but in the context of recent technological advances, such as next-generation sequencing and high-throughput screens, and their applications. This enables readers to move rapidly from the b |
| introduction to computational molecular biology: Algorithms in Structural Molecular Biology Bruce R. Donald, 2023-08-15 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. |
| introduction to computational molecular biology: Research in Computational Molecular Biology Lenore J. Cowen, 2019-04-15 This book constitutes the proceedings of the 23rd Annual Conference on Research in Computational Molecular Biology, RECOMB 2019, held in Washington, DC, USA, in April 2019. The 17 extended and 20 short abstracts presented were carefully reviewed and selected from 175 submissions. The short abstracts are included in the back matter of the volume. The papers report on original research in all areas of computational molecular biology and bioinformatics. |
| introduction to computational molecular biology: Learning and Inference in Computational Systems Biology Neil D. Lawrence, 2010 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 |
| introduction to computational molecular biology: Algorithms in Computational Molecular Biology Mourad Elloumi, Albert Y. Zomaya, 2011-04-04 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. |
| introduction to computational molecular biology: Fundamentals of Bioinformatics and Computational Biology Gautam B. Singh, 2014-09-24 This book offers comprehensive coverage of all the core topics of bioinformatics, and includes practical examples completed using the MATLAB bioinformatics toolboxTM. It is primarily intended as a textbook for engineering and computer science students attending advanced undergraduate and graduate courses in bioinformatics and computational biology. The book develops bioinformatics concepts from the ground up, starting with an introductory chapter on molecular biology and genetics. This chapter will enable physical science students to fully understand and appreciate the ultimate goals of applying the principles of information technology to challenges in biological data management, sequence analysis, and systems biology. The first part of the book also includes a survey of existing biological databases, tools that have become essential in today’s biotechnology research. The second part of the book covers methodologies for retrieving biological information, including fundamental algorithms for sequence comparison, scoring, and determining evolutionary distance. The main focus of the third part is on modeling biological sequences and patterns as Markov chains. It presents key principles for analyzing and searching for sequences of significant motifs and biomarkers. The last part of the book, dedicated to systems biology, covers phylogenetic analysis and evolutionary tree computations, as well as gene expression analysis with microarrays. In brief, the book offers the ideal hands-on reference guide to the field of bioinformatics and computational biology. |
| introduction to computational molecular biology: Mining the Biomedical Literature Hagit Shatkay, Mark Craven, 2012 The authors offer an accessible introduction to key ideas in biomedical text mining. The chapters cover such topics as the sources of biomedical text; text-analysis methods in natural language processing; the tasks of information extraction, information retrieval, and text categorization; and methods for empirically assessing textmining systems. |
| introduction to computational molecular biology: Introduction to Computational Biology Bernhard Haubold, Thomas Wiehe, 2009-09-03 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. |
| introduction to computational molecular biology: Statistical Modeling and Machine Learning for Molecular Biology Alan Moses, 2017-01-06 • 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 |
| introduction to computational molecular biology: Computational Systems Biology Andres Kriete, Roland Eils, 2013-11-26 This comprehensively revised second edition of Computational Systems Biology discusses the experimental and theoretical foundations of the function of biological systems at the molecular, cellular or organismal level over temporal and spatial scales, as systems biology advances to provide clinical solutions to complex medical problems. In particular the work focuses on the engineering of biological systems and network modeling. - Logical information flow aids understanding of basic building blocks of life through disease phenotypes - Evolved principles gives insight into underlying organizational principles of biological organizations, and systems processes, governing functions such as adaptation or response patterns - Coverage of technical tools and systems helps researchers to understand and resolve specific systems biology problems using advanced computation - Multi-scale modeling on disparate scales aids researchers understanding of dependencies and constraints of spatio-temporal relationships fundamental to biological organization and function. |
| introduction to computational molecular biology: Introduction to Computational Science Angela B. Shiflet, George W. Shiflet, 2014-03-30 The essential introduction to computational science—now fully updated and expanded Computational science is an exciting new field at the intersection of the sciences, computer science, and mathematics because much scientific investigation now involves computing as well as theory and experiment. This textbook provides students with a versatile and accessible introduction to the subject. It assumes only a background in high school algebra, enables instructors to follow tailored pathways through the material, and is the only textbook of its kind designed specifically for an introductory course in the computational science and engineering curriculum. While the text itself is generic, an accompanying website offers tutorials and files in a variety of software packages. This fully updated and expanded edition features two new chapters on agent-based simulations and modeling with matrices, ten new project modules, and an additional module on diffusion. Besides increased treatment of high-performance computing and its applications, the book also includes additional quick review questions with answers, exercises, and individual and team projects. The only introductory textbook of its kind—now fully updated and expanded Features two new chapters on agent-based simulations and modeling with matrices Increased coverage of high-performance computing and its applications Includes additional modules, review questions, exercises, and projects An online instructor's manual with exercise answers, selected project solutions, and a test bank and solutions (available only to professors) An online illustration package is available to professors |
| introduction to computational molecular biology: Computational Biology Röbbe Wünschiers, 2012-12-06 -Teaches the reader how to use Unix, which is the key to basic computing and allows the most flexibility for bioinformatics applications -Written specifically with the needs of molecular biologists in mind -Easy to follow, written for beginners with no computational knowledge -Includes examples from biological data analysis -Can be use either for self-teaching or in courses |
| introduction to computational molecular biology: Algorithms on Strings, Trees, and Sequences Dan Gusfield, 1997-05-28 String algorithms are a traditional area of study in computer science. In recent years their importance has grown dramatically with the huge increase of electronically stored text and of molecular sequence data (DNA or protein sequences) produced by various genome projects. This book is a general text on computer algorithms for string processing. In addition to pure computer science, the book contains extensive discussions on biological problems that are cast as string problems, and on methods developed to solve them. It emphasises 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. Its discussion of current algorithms and techniques also makes it a reference for professionals. |
| introduction to computational molecular biology: Computational Biology Ralf Blossey, 2006-05-25 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. |
| introduction to computational molecular biology: Introduction to Bioinformatics Anna Tramontano, 2018-10-03 Guiding readers from the elucidation and analysis of a genomic sequence to the prediction of a protein structure and the identification of the molecular function, Introduction to Bioinformatics describes the rationale and limitations of the bioinformatics methods and tools that can help solve biological problems. Requiring only a limited mathematical and statistical background, the book shows how to efficiently apply these approaches to biological data and evaluate the resulting information. The author, an expert bioinformatics researcher, first addresses the ways of storing and retrieving the enormous amount of biological data produced every day and the methods of decrypting the information encoded by a genome. She then covers the tools that can detect and exploit the evolutionary and functional relationships among biological elements. Subsequent chapters illustrate how to predict the three-dimensional structure of a protein. The book concludes with a discussion of the future of bioinformatics. Even though the future will undoubtedly offer new tools for tackling problems, most of the fundamental aspects of bioinformatics will not change. This resource provides the essential information to understand bioinformatics methods, ultimately facilitating in the solution of biological problems. |
| introduction to computational molecular biology: Computer Simulation and Data Analysis in Molecular Biology and Biophysics Victor Bloomfield, 2009-06-05 This book provides an introduction to two important aspects of modern bioch- istry, molecular biology, and biophysics: computer simulation and data analysis. My aim is to introduce the tools that will enable students to learn and use some f- damental methods to construct quantitative models of biological mechanisms, both deterministicandwithsomeelementsofrandomness;tolearnhowconceptsofpr- ability can help to understand important features of DNA sequences; and to apply a useful set of statistical methods to analysis of experimental data. The availability of very capable but inexpensive personal computers and software makes it possible to do such work at a much higher level, but in a much easier way, than ever before. TheExecutiveSummaryofthein?uential2003reportfromtheNationalAcademy of Sciences, “BIO 2010: Transforming Undergraduate Education for Future - search Biologists” [12], begins The interplay of the recombinant DNA, instrumentation, and digital revolutions has p- foundly transformed biological research. The con?uence of these three innovations has led to important discoveries, such as the mapping of the human genome. How biologists design, perform, and analyze experiments is changing swiftly. Biological concepts and models are becoming more quantitative, and biological research has become critically dependent on concepts and methods drawn from other scienti?c disciplines. The connections between the biological sciences and the physical sciences, mathematics, and computer science are rapidly becoming deeper and more extensive. |
| introduction to computational molecular biology: Computational Molecular Biology Peter Clote, Rolf Backofen, 2000-10-03 Molecular biology. Math primer. Sequence alignment. All about eve. Hidden markov models. Structure prediction. A mathematical background. Resources. |
| introduction to computational molecular biology: Research in Computational Molecular Biology Vineet Bafna, S. Cenk Sahinalp, 2011-03-24 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. |
| introduction to computational molecular biology: AN INTRODUCTION TO COMPUTATIONAL BIOCHEMISTRY Tsai, 2007 Market_Desc: · Primary: Students in Biochemistry, Bioinformatics, Biotechnology or Bioengineering, Computational Sciences, Chemistry, Molecular Biology· Secondary: Researchers in the above fields· Tertiary: Students and researchers in Computer science, Biomedical fields Special Features: · The book comprises of separate chapters that include problem sets and answers as well as an introduction, review of biological concepts, discussion of the programming and application, and key references· It also includes an appendix listing software suppliers· All programs cited in the book have been carefully chosen based on ease of installation and operation· Reviews the fundamental biochemical principles About The Book: This book provides a comprehensive introduction to biochemical principles and the skills required for applying computational tools to practical problems in biochemistry. It also details biochemical structures, enzyme reactions, metabolic simulation, genomic and proteomic analyses, and molecular modeling, as well as demonstrating the broad applications and versatility of computers for use in solving biochemical problems. |
| introduction to computational molecular biology: Molecular Biology - Not Only for Bioinformaticians Wiesława Widłak, 2013-12-05 Bioinformatics, which can be defined as the application of computer science and information technology to the field of biology and medicine, has been rapidly developing over the past few decades. It generates new knowledge as well as the computational tools to create that knowledge. Understanding the basic processes in living organisms is therefore indispensable for bioinformaticians. This book addresses beginners in molecular biology, especially computer scientists who would like to work as bioinformaticians. It presents basic processes in living organisms in a condensed manner. Additionally, principles of several high-throughput technologies in molecular biology, which need the assistance of bioinformaticians, are explained from a biological point of view. It is structured in the following 9 chapters: cells and viruses; protein structure and function; nucleic acids; DNA replication, mutations, and repair; transcription and posttranscriptional processes; synthesis and posttranslational modifications of proteins; cell division; cell signaling pathways; and high-throughput technologies in molecular biology. |
| introduction to computational molecular biology: Fundamentals of Molecular Structural Biology Subrata Pal, 2019-08-13 Fundamentals of Molecular Structural Biology reviews the mathematical and physical foundations of molecular structural biology. Based on these fundamental concepts, it then describes molecular structure and explains basic genetic mechanisms. Given the increasingly interdisciplinary nature of research, early career researchers and those shifting into an adjacent field often require a fundamentals book to get them up-to-speed on the foundations of a particular field. This book fills that niche. - Provides a current and easily digestible resource on molecular structural biology, discussing both foundations and the latest advances - Addresses critical issues surrounding macromolecular structures, such as structure-based drug discovery, single-particle analysis, computational molecular biology/molecular dynamic simulation, cell signaling and immune response, macromolecular assemblies, and systems biology - Presents discussions that ultimately lead the reader toward a more detailed understanding of the basis and origin of disease |
| introduction to computational molecular biology: Computational Biology Scott T. Kelley, Dennis Didulo, 2018-01-01 This textbook is for anyone who needs to learn the basics of bioinformatics—the use of computational methods to better understand biological systems. Computational Biology covers the principles and applications of the computational methods used to study DNA, RNA, and proteins, including using biological databases such as NCBI and UniProt; performing BLAST, sequence alignments, and structural predictions; and creating phylogenetic trees. It includes a primer that can be used as a jumping off point for learning computer programming for bioinformatics. This text can be used as a self-study guide, as a course focused on computational methods in biology/bioinformatics, or to supplement general courses that touch on topics included within the book. Computational Biology's robust interactive online components “gamify” the study of bioinformatics, allowing the reader to practice randomly generated problems on their own time to build confidence and skill and gain practical real-world experience. The online component also assures that the content being taught is up to date and accurately reflects the ever-changing landscape of bioinformatics web-based programs. |
| introduction to computational molecular biology: Bioinformatics and Computational Biology Basant K. Tiwary, 2021-11-24 This textbook introduces fundamental concepts of bioinformatics and computational biology to the students and researchers in biology, medicine, veterinary science, agriculture, and bioengineering . The respective chapters provide detailed information on biological databases, sequence alignment, molecular evolution, next-generation sequencing, systems biology, and statistical computing using R. The book also presents a case-based discussion on clinical, veterinary, agricultural bioinformatics, and computational bioengineering for application-based learning in the respective fields. Further, it offers readers guidance on reconstructing and analysing biological networks and highlights computational methods used in systems medicine and genome-wide association mapping of diseases. Given its scope, this textbook offers an essential introductory book on bioinformatics and computational biology for undergraduate and graduate students in the life sciences, botany, zoology, physiology, biotechnology, bioinformatics, and genomic science as well as systems biology, bioengineering and the agricultural, and veterinary sciences. |
| introduction to computational molecular biology: Computational Structural Biology: Methods And Applications Torsten Schwede, Manuel C Peitsch, 2008-05-02 Computational structural biology has made tremendous progress over the last two decades, and this book provides a recent and broad overview of such computational methods in structural biology. It covers the impact of computational structural biology on protein structure prediction methods, macromolecular function and protein design, and key methods in drug discovery. It also addresses the computational challenges of experimental approaches in structural biology. In addition to reviewing the current state of computational structural biology, each chapter ends with a brief, visionary discussion on the future outlook, whereby the main challenges for the coming years are elucidated. Written by an international panel of expert contributors, this book can serve as a reference manual for students and practitioners alike. |
| introduction to computational molecular biology: Bioinformatics Algorithms Phillip Compeau, Pavel Pevzner, 1986-06 Bioinformatics Algorithms: an Active Learning Approach is one of the first textbooks to emerge from the recent Massive Online Open Course (MOOC) revolution. A light-hearted and analogy-filled companion to the authors' acclaimed online course (http://coursera.org/course/bioinformatics), this book presents students with a dynamic approach to learning bioinformatics. It strikes a unique balance between practical challenges in modern biology and fundamental algorithmic ideas, thus capturing the interest of students of biology and computer science students alike.Each chapter begins with a central biological question, such as Are There Fragile Regions in the Human Genome? or Which DNA Patterns Play the Role of Molecular Clocks? and then steadily develops the algorithmic sophistication required to answer this question. Hundreds of exercises are incorporated directly into the text as soon as they are needed; readers can test their knowledge through automated coding challenges on Rosalind (http://rosalind.info), an online platform for learning bioinformatics.The textbook website (http://bioinformaticsalgorithms.org) directs readers toward additional educational materials, including video lectures and PowerPoint slides. |
| introduction to computational molecular biology: Computational Methods in Molecular Biology Steven L. Salzberg, David B. Searls, Simon Kasif, 1998 Briefly describes software and databases for computational biology on the Internet and offers suggestions for further reading. Annotation copyrighted by Book News, Inc., Portland, OR. |
| introduction to computational molecular biology: Computational Molecular Evolution Ziheng Yang, 2006-10-05 This book describes the models, methods and algorithms that are most useful for analysing the ever-increasing supply of molecular sequence data, with a view to furthering our understanding of the evolution of genes and genomes. |
| introduction to computational molecular biology: Principles of Computational Cell Biology Volkhard Helms, 2019-04-29 Computational cell biology courses are increasingly obligatory for biology students around the world but of course also a must for mathematics and informatics students specializing in bioinformatics. This book, now in its second edition is geared towards both audiences. The author, Volkhard Helms, has, in addition to extensive teaching experience, a strong background in biology and informatics and knows exactly what the key points are in making the book accessible for students while still conveying in depth knowledge of the subject.About 50% of new content has been added for the new edition. Much more room is now given to statistical methods, and several new chapters address protein-DNA interactions, epigenetic modifications, and microRNAs. |
| introduction to computational molecular biology: Biological Sequence Analysis Richard Durbin, 1998-04-23 Presents up-to-date computer methods for analysing DNA, RNA and protein sequences. |
| introduction to computational molecular biology: Computational Biology And Genome Informatics Paul P Wang, Jason T L Wang, Cathy H Wu, 2003-02-19 This book contains articles written by experts on a wide range of topics that are associated with the analysis and management of biological information at the molecular level. It contains chapters on RNA and protein structure analysis, DNA computing, sequence mapping, genome comparison, gene expression data mining, metabolic network modeling, and phyloinformatics.The important work of some representative researchers in bioinformatics is brought together for the first time in one volume. The topic is treated in depth and is related to, where applicable, other emerging technologies such as data mining and visualization. The goal of the book is to introduce readers to the principle techniques of bioinformatics in the hope that they will build on them to make new discoveries of their own. |
| introduction to computational molecular biology: Biological Computation Ehud Lamm, Ron Unger, 2011-05-25 The area of biologically inspired computing, or biological computation, involves the development of new, biologically based techniques for solving difficult computational problems. A unified overview of computer science ideas inspired by biology, Biological Computation presents the most fundamental and significant concepts in this area. In the book, students discover that bacteria communicate, that DNA can be used for performing computations, how evolution solves optimization problems, that the way ants organize their nests can be applied to solve clustering problems, and what the human immune system can teach us about protecting computer networks. The authors discuss more biological examples such as these, along with the computational techniques developed from these scenarios. The text focuses on cellular automata, evolutionary computation, neural networks, and molecular computation. Each chapter explores the biological background, describes the computational techniques, gives examples of applications, discusses possible variants of the techniques, and includes exercises and solutions. The authors use the examples and exercises to illustrate key ideas and techniques. Clearly conveying the essence of the major computational approaches in the field, this book brings students to the point where they can either produce a working implementation of the techniques or effectively use one of the many available implementations. Moreover, the techniques discussed reflect fundamental principles that can be applied beyond bio-inspired computing. Supplementary material is available on Dr. Unger's website. |
INTRODUCTION Definition & Meaning - Merriam-Webster
The meaning of INTRODUCTION is something that introduces. How to use introduction in a sentence.
How to Write an Introduction, With Examples | Grammarly
Oct 20, 2022 · An introduction should include three things: a hook to interest the reader, some background on the topic so the reader can understand it, and a thesis statement that clearly …
INTRODUCTION | English meaning - Cambridge Dictionary
INTRODUCTION definition: 1. an occasion when something is put into use or brought to a place for the first time: 2. the act…. Learn more.
What Is an Introduction? Definition & 25+ Examples - Enlightio
Nov 5, 2023 · An introduction is the initial section of a piece of writing, speech, or presentation wherein the author presents the topic and purpose of the material. It serves as a gateway for …
Introduction - definition of introduction by The Free Dictionary
Something spoken, written, or otherwise presented in beginning or introducing something, especially: a. A preface, as to a book. b. Music A short preliminary passage in a larger …
INTRODUCTION Definition & Meaning - Merriam-Webster
The meaning of INTRODUCTION is something that introduces. How to use introduction in a sentence.
How to Write an Introduction, With Examples | Grammarly
Oct 20, 2022 · An introduction should include three things: a hook to interest the reader, some background on the topic so the reader can understand it, and a thesis statement that clearly …
INTRODUCTION | English meaning - Cambridge Dictionary
INTRODUCTION definition: 1. an occasion when something is put into use or brought to a place for the first time: 2. the act…. Learn more.
What Is an Introduction? Definition & 25+ Examples - Enlightio
Nov 5, 2023 · An introduction is the initial section of a piece of writing, speech, or presentation wherein the author presents the topic and purpose of the material. It serves as a gateway for …
Introduction - definition of introduction by The Free Dictionary
Something spoken, written, or otherwise presented in beginning or introducing something, especially: a. A preface, as to a book. b. Music A short preliminary passage in a larger …
