Literature for Bioinfomatics
| | Molecular Evolution and Phylogenetics By Masatoshi Nei, Sudhir Kumar,
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Book Description:
During the last ten years, remarkable progress has occurred in the study of molecular evolution. Among the most important factors that are responsible for this progress are the development of new statistical methods and advances in computational technology. In particular, phylogenetic analysis of DNA or protein sequences has become a powerful tool for studying molecular evolution. Along with this developing technology, the application of the new statistical and computational methods has become more complicated and there is no comprehensive volume that treats these methods in depth. Molecular Evolution and Phylogenetics fills this gap and present various statistical methods that are easily accessible to general biologists as well as biochemists, bioinformatists and graduate students. The text covers measurement of sequence divergence, construction of phylogenetic trees, statistical tests for detection of positive Darwinian selection, inference of ancestral amino acid sequences, construction of linearized trees, and analysis of allele frequency data. Emphasis is given to practical methods of data analysis, and methods can be learned by working through numerical examples using the computer program MEGA2 that is provided.
| | Probabilistic Modelling in Bioinformatics and Medical Informatics By
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Book Description:
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
| | Sequence - Evolution - Function: Computational Approaches in Comparative Genomics By Eugene V. Koonin, Michael Y. Galperin,
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Book Description:
Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as functional genomics. The book provides the reader with an understanding of the principles and approaches of functional genomics and of the potential and limitations of computational and experimental approaches to genome analysis.
Key topics covered in this textbook are:
*the completed and ongoing genome sequencing projects,
*databases that store and organize genomic data, with their unique advantages and pitfalls,
*principles and methods of genome analysis and annotation,
*ways to automate the searches and increase search sensitivity while minimizing the error rate,
*the first lessons from the Human Genome Project,
*the contribution of comparative genomics to the understanding of hereditary diseases and cancer,
*fundamental and practical applications of comparative genomics,
*the use of complete genomes for evolutionary analysis,
*the application of comparative genomics for identification of potential drug targets in microbial genomes,
*Problems for Further Study, which are designed to be solved by using methods available through the WWW.
| | Bioinformatics of Genome Regulation and Structure II By
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Book Description:
This work is a follow-up of the International Conference on Bioinformatics of Genome Regulation and Structure (BGRS-2004), held in Novosibirsk, Russia, in July 2004. It comprises the newest results obtained while researching into the structure and function of molecular genetic systems belonging to different complexity level of their organization. The material covers the following: (i) regulatory genomic sequences; (ii) large-scale genome analysis and functional annotation; (iii) gene structure detection and prediction; (iv) comparative and evolutionary genomics; (v) computer analysis of genome polymorphism and evolution; computer analysis and modeling of transcription, splicing, and translation; structural computational biology; (vi) gene networks, signal transduction pathways, and genetically controlled metabolic pathways; (vii) data warehousing, knowledge discovery and data mining; and (viii) analysis of basic patterns of genome operation, organization, and evolution.
| | Computational Genome Analysis: An Introduction (Statistics for Biology & Health) By Richard C. Deonier, Simon Tavaré, Michael S. Waterman,
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Book Description:
Computational Genome Analysis: An Introduction presents the foundations of key problems in computational molecular biology and bioinformatics. It focuses on computational and statistical principles applied to genomes, and introduces the mathematics and statistics that are crucial for understanding these applications. The book is appropriate for a one-semester course for advanced undergraduate or beginning graduate students, and it can also introduce computational biology to computer scientists, mathematicians, or biologists who are extending their interests into this exciting field.
This book features:
Topics organized around biological problems, such as sequence alignment and assembly, DNA signals, analysis of gene expression, and human genetic variation
Presentation of fundamentals of probability, statistics, and algorithms
Implementation of computational methods with numerous examples based upon the R statistics package
Extensive descriptions and explanations to complement the analytical development
More than 100 illustrations and diagrams (some in color) to reinforce concepts and present key results from the primary literature
Exercises at the end of chapters
Michael S. Waterman is a University Professor, a USC Associates Chair in Natural Sciences, and Professor of Biological Sciences, Computer Science, and Mathematics at the University of Southern California. A member of the National Academy of Sciences and the American Academy of Arts and Sciences, Professor Waterman is Founding Editor and Co-Editor in Chief of the Journal of Computational Biology. His research has focused on computational analysis of molecular sequence data. His best-known work is the co-development of the local alignment Smith-Waterman algorithm, which has become the foundational tool for database search methods. His interests have also encompassed physical mapping, as exemplified by the Lander-Waterman formulas, and genome sequence assembly using an Eulerian path method.
Simon Tavaré holds the George and Louise Kawamoto Chair in Biological Sciences and is a Professor of Biological Sciences, Mathematics, and Preventive Medicine at the University of Southern California. Professor Tavaré's research lies at the interface between statistics and biology, specifically focusing on problems arising in molecular biology, human genetics, population genetics, molecular evolution, and bioinformatics. His statistical interests focus on stochastic computation. Among the applications are linkage disequilibrium mapping, stem cell evolution, and inference in the fossil record. Dr. Tavaré is also a professor in the Department of Oncology at the University of Cambridge, England, where his group concentrates on cancer genomics.
Richard C. Deonier is Professor Emeritus in the Molecular and Computational Biology Section of the Department of Biological Sciences at the University of Southern California. Originally trained as a physical biochemist, His major research has been in areas of molecular genetics, with particular interests in physical methods for gene mapping, bacterial transposable elements, and conjugative plasmids. During 30 years of active teaching, he has taught chemistry, biology, and computational biology at both the undergraduate and graduate levels.
| | Fundamentals of Data Mining in Genomics and Proteomics By
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| | Genomics, Proteomics and Vaccines By
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Book Description:
While the sequence of the human genome sequence has hit the headlines, extensive exploitation of this for practical applications is still to come. Genomic and post-genomic technologies applied to viral and bacterial pathogens, which are almost equally important from a scientific perspective, have the potential to be translated into useful products and processes much more rapidly.
Genomics, Proteomics and Vaccines introduces the history of vaccinology and discusses how vaccines are expected to evolve in the future. It describes the relevant technologies, including genome sequencing and analysis, DNA microarrays, 2D electrophoresis and 2D chromatography, mass spectrometry and high-throughput protein expression and purification. The book also features examples of the exploitation of genomics and post-genomics in vaccine discovery, and contains useful descriptions of the biology and pathogenesis of clinically important bacterial pathogens.
This book should be of interest to all those working in vaccine discovery and development in pharmaceutical and biotechnology companies as well as in academic institutions
| | In Silico Immunology By
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Book Description:
Theoretical immunology is the application of mathematical modeling to diverse aspects of immunology. Immunoinformatics, the application of computational informatics to the study of immunological macromolecules, addresses questions in immunobiology and vaccinology, as well as addressing issues of data management, and can design and implement new experimental strategies. Artificial Immune Systems (AIS) uses ideas and concepts from immunology to guide and inspire new algorithms, data structures, and software development.
These three different disciplines are now poised to engineer a paradigm shift from hypothesis- to data-driven research, with new understanding emerging from the analysis of complex datasets: theoretical immunology, immunoinformatics, and Artificial Immune Systems (AIS). "in silico Immunology" will summarize these emergent disciplines and will address the issue of synergy as it shows how these three are set to transform immunological science and the future of health care.
| | Introduction to Computational Biology: An Evolutionary Approach By Bernhard Haubold, Thomas Wiehe,
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Book Description:
Analysis of molecular sequence data is the main subject of this introduction to computational biology. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models. In the second part evolutionary time takes center stage and phylogenetic reconstruction, the analysis of sequence variation, and the dynamics of genes in populations are explained in detail. In addition, the book contains a computer program with a graphical user interface that allows the reader to experiment with a number of key concepts developed by the authors. This textbook is intended for students enrolled in courses in computational biology or bioinformatics as well as for molecular biologists, mathematicians, and computer scientists.
| | Introduction to Mathematical Methods in Bioinformatics (Universitext) By Alexander Isaev
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Book Description:
This book looks at the mathematical foundations of the models currently in use. This is crucial for the correct interpretation of the outputs of the models. A bioinformatician should be able not only to use software packages, but also to know the mathematics behind these packages.
From this point of view, mathematics departments throughout the world have a major role to play in bioinformatics education by teaching courses on the mathematical foundations of the subject. Based on the courses taught by the author the book combines several topics in biological sequence analysis with mathematical and statistical material required for such analysis.
| | Java for Bioinformatics and Biomedical Applications By Harshawardhan Bal, Johnny Hujol,
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| | Bioinformatics: A Biologist's Guide to Biocomputing and the Internet By Stuart M. Brown
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| | Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health) By Warren J. Ewens, Gregory Grant,
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Book Description:
Advances in computers and biotechnology have had a profound impact on biomedical research, and as a result complex data sets can now be generated to address extremely complex biological questions. Correspondingly, advances in the statistical methods necessary to analyze such data are following closely behind the advances in data generation methods. The statistical methods required by bioinformatics present many new and difficult problems for the research community.
This book provides an introduction to some of these new methods. The main biological topics treated include sequence analysis, BLAST, microarray analysis, gene finding, and the analysis of evolutionary processes. The main statistical techniques covered include hypothesis testing and estimation, Poisson processes, Markov models and Hidden Markov models, and multiple testing methods.
The second edition features new chapters on microarray analysis and on statistical inference, including a discussion of ANOVA, and discussions of the statistical theory of motifs and methods based on the hypergeometric distribution. Much material has been clarified and reorganized.
The book is written so as to appeal to biologists and computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved with bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level, but with an emphasis on material relevant to later chapters and often not covered in standard introductory texts. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematical background consists of introductory courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context, and standard mathematical concepts are summarized in an Appendix. Problems are provided at the end of each chapter allowing the reader to develop aspects of the theory outlined in the main text.
Warren J. Ewens holds the Christopher H. Brown Distinguished Professorship at the University of Pennsylvania. He is the author of two books, Population Genetics and Mathematical Population Genetics. He is a senior editor of Annals of Human Genetics and has served on the editorial boards of Theoretical Population Biology, GENETICS, Proceedings of the Royal Society B and SIAM Journal in Mathematical Biology. He is a fellow of the Royal Society and the Australian Academy of Science.
Gregory R. Grant is a senior bioinformatics researcher in the University of Pennsylvania Computational Biology and Informatics Laboratory. He obtained his Ph.D. in number theory from the University of Maryland in 1995 and his Masters in Computer Science from the University of Pennsylvania in 1999.
Comments on the First Edition. "This book would be an ideal text for a postgraduate course…[and] is equally well suited to individual study…. I would recommend the book highly" (Biometrics). "Ewens and Grant have given us a very welcome introduction to what is behind those pretty [graphical user] interfaces" (Naturwissenschaften.). "The authors do an excellent job of presenting the essence of the material without getting bogged down in mathematical details" (Journal. American Staistical. Association). "The authors have restructured classical material to a great extent and the new organization of the different topics is one of the outstanding services of the book" (Metrika).
| | Optimized Bayesian Dynamic Advising: Theory and Algorithms (Advanced Information and Knowledge Processing) By
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Book Description:
Written by one of the world’s leading groups in the area of Bayesian identification, control, and decision making, this book provides the theoretical and algorithmic basis of optimized probabilistic advising.
Starting from abstract ideas and formulations, and culminating in detailed algorithms, the book comprises a unified treatment of an important problem of the design of advisory systems supporting supervisors of complex processes. It introduces the theoretical and algorithmic basis of developed advising, relying on novel and powerful combination black-box modeling by dynamic mixture models and fully probabilistic dynamic optimization.
Written for a broad audience, including developers of algorithms and application engineers, researchers, lecturers, and postgraduates, this book can be used as a reference tool, and an advanced text on Bayesian dynamic decision making. A CD contains a specialized Matlab-based Mixtools toolbox, and examples illustrating the most important and complex areas of the material presented.
| | Physics in Molecular Biology By Kim Sneppen, Giovanni Zocchi,
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Book Description:
Tools developed by statistical physicists are of increasing importance in the analysis of complex biological systems. Physics in Molecular Biology discusses how physics can be used in modeling life. It begins by summarizing important biological concepts, emphasizing how they differ from the systems normally studied in physics. A variety of topics, ranging from the properties of single molecules to the dynamics of macro-evolution, are studied in terms of simple mathematical models. The main focus of the book is on genes and proteins and how they build systems that compute and respond. The discussion develops from simple to complex systems, and from small-scale to large-scale phenomena. This book will inspire advanced undergraduates and graduate students in physics to approach biological subjects from a physicist’s point of view. It is self-contained, requiring no background knowledge of biology, and only familiarity with basic concepts from physics, such as forces, energy, and entropy.
| | Advanced Molecular Biology: A Concise Reference By R. M. Twyman
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| | Frontiers in Polar Biology in the Genomic Era By
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| | Data Mining in Bioinformatics (Advanced Information and Knowledge Processing) By
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Book Description:
The goal of this book is to help readers understand state-of-the-art techniques in biological data mining and data management and includes topics such as:
- preprocessing tasks such as data cleaning and data integration as applied to biological data
- classification and clustering techniques for microarrays
- comparison of RNA structures based on string properties and energetics
- discovery of the sequence characteristics of different parts of the genome
- mining of haplotypes to find disease markers
- sequencing of events leading to the folding of a protein
- inference of the subcellular location of protein activity
- classification of chemical compounds based on structure
- special purpose metrics and index structures for phylogenetic applications
- a new query language for protein searching based on the shape of proteins
- very fast indexing schemes for sequences and pathways
Aimed at computer scientists, necessary biology is explained.
| | Evolutionary Computation in Data Mining (Studies in Fuzziness and Soft Computing) By
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Book Description:
This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. Evolutionary Computation in Data Mining provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics.
| | Bioinformatics Technologies By
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Book Description:
Solving modern biological problems requires advanced computational methods. Bioinformatics evolved from the active interaction of two fast-developing disciplines, biology and information technology. The central issue of this emerging field is the transformation of often distributed and unstructured biological data into meaningful information.
This book describes the application of well-established concepts and techniques from areas like data mining, machine learning, database technologies, and visualization techniques to problems like protein data analysis, genome analysis and sequence databases. Chen has collected contributions from leading researchers in each area. The chapters can be read independently, as each offers a complete overview of its specific area, or, combined, this monograph is a comprehensive treatment that will appeal to students, researchers, and R&D professionals in industry who need a state-of-the-art introduction into this challenging and exciting young field.
| | Machine Learning and Data Mining for Computer Security: Methods and Applications (Advanced Information and Knowledge Processing) By
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Book Description:
"Machine Learning and Data Mining for Computer Security" provides an overview of the current state of research in machine learning and data mining as it applies to problems in computer security. This book has a strong focus on information processing and combines and extends results from computer security.
The first part of the book surveys the data sources, the learning and mining methods, evaluation methodologies, and past work relevant for computer security. The second part of the book consists of articles written by the top researchers working in this area. These articles deals with topics of host-based intrusion detection through the analysis of audit trails, of command sequences and of system calls as well as network intrusion detection through the analysis of TCP packets and the detection of malicious executables.
This book fills the great need for a book that collects and frames work on developing and applying methods from machine learning and data mining to problems in computer security.
| | Probabilistic Modelling in Bioinformatics and Medical Informatics By
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Book Description:
Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.
| | Medical Informatics: Knowledge Management and Data Mining in Biomedicine (Integrated Series in Information Systems) By
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Book Description:
Medical Informatics and biomedical computing have grown in quantum measure over the past decade. An abundance of advances have come to the foreground in this field with the vast amounts of biomedical and genomic data, the Internet, and the wide application of computer use in all aspects of medical, biological, and health care research and practice. MEDICAL INFORMATICS: Knowledge Management and Data Mining in Biomedicine covers the basic foundations of the area while extending the foundational material to include the recent leading-edge research in the field. The newer concepts, techniques, and practices of biomedical knowledge management and data mining are introduced and examined in detail. It is the research and applications in these areas that are raising the technical horizons and expanding the utility of informatics to an increasing number of biomedical professionals and researchers.
The book is divided into three major topical sections.
Section I presents the foundational information and knowledge management material and includes topics such as: bioinformatics challenges and standards, security and privacy, ethical and social issues, and biomedical knowledge mapping.
Section II discusses the topics which are relevant to knowledge representations & access and includes topics such as: representations of medical concepts and relationships, genomic information retrieval, 3D medical informatics, public access to anatomic images, and creating and maintaining biomedical ontologies.
Section III examines the emerging application research in data mining, biomedical textual mining, and knowledge discovery research and includes topics such as: semantic parsing and analysis for patient records, biological relationships, gene pathways, and metabolic networks, exploratory genomic data analysis, joint learning using data and text mining, and disease informatics and outbreak detection.
The book is a comprehensive presentation of the foundations and leading application research in medical informatics/biomedicine. These concepts and techniques are illustrated with detailed case studies.
The authors are widely recognized professors and researchers in Schools of Medicine and Information Systems from the University of Arizona, University of Washington, Columbia University, and Oregon Health & Science University. In addition, individual expert contributing authors have been commissioned to write chapters for the book on their respective topical expertise.
| | Modeling Biological Systems:: Principles and Applications By James W. Haefner
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Book Description:
This is the second edition of a textbook currently published by Springer for a course in mathematical modeling and computer simulation for biologists at the advanced undergraduate and introductory graduate level. The audience for this edition is similar to that of the previous one: advanced level courses in computational biology, as well as researchers retooling themselves. This new edition includes a CD-ROM with real examples of models as teaching tools.
| | Proteomics and Protein-Protein Interactions: Biology, Chemistry, Bioinformatics, and Drug Design (Protein Reviews) By
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Book Description:
The rapidly evolving field of protein science has now come to realize the ubiquity and importance of protein-protein interactions. It had been known for some time that proteins may interact with each other to form functional complexes, but it was thought to be the property of only a handful of key proteins. However, with the advent of high throughput proteomics to monitor protein-protein interactions at an organism level, we can now safely state that protein-protein interactions are the norm and not the exception. Thus, protein function must be understood in the larger context of the various binding complexes that each protein may form with interacting partners at a given time in the life cycle of a cell. Proteins are now seen as forming sophisticated interaction networks subject to remarkable regulation. The study of these interaction networks and regulatory mechanism, which I would like to term "systems proteomics," is one of the thriving fields of proteomics. The bird-eye view that systems proteomics offers should not however mask the fact that proteins are each characterized by a unique set of physical and chemical properties. In other words, no protein looks and behaves like another. This complicates enormously the design of high-throughput proteomics methods. Unlike genes, which, by and large, display similar physico-chemical behaviors and thus can be easily used in a high throughput mode, proteins are not easily amenable to the same treatment. It is thus important to remind researchers active in the proteomics field the fundamental basis of protein chemistry. This book attempts to bridge the two extreme ends of protein science: on one end, systems proteomics, which describes, at a system level, the intricate connection network that proteins form in a cell, and on the other end, protein chemistry and biophysics, which describe the molecular properties of individual proteins and the structural and thermodynamic basis of their interactions within the network. Bridging the two ends of the spectrum is bioinformatics and computational chemistry. Large data sets created by systems proteomics need to be mined for meaningful information, methods need to be designed and implemented to improve experimental designs, extract signal over noise, and reject artifacts, and predictive methods need to be worked out and put to the test. Computational chemistry faces similar challenges. The prediction of binding thermodynamics of protein-protein interaction is still in its infancy. Proteins are large objects, and simplifying assumptions and shortcuts still need to be applied to make simulations manageable, and this despite exponential progress in computer technology. Finally, the study of proteins impacts directly on human health. It is an obvious statement to say that, for decades, enzymes, receptors, and key regulator proteins have been targeted for drug discovery. However, a recent and exciting development is the exploitation of our knowledge of protein-protein interaction for the design of new pharmaceuticals. This presents particular challenges because protein-protein interfaces are generally shallow and interactions are weak. However, progress is clearly being made and the book seeks to provide examples of successes in this area.
| | Statistical Methods in Molecular Evolution (Statistics for Biology and Health) By
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Book Description:
In the field of molecular evolution, inferences about past evolutionary events are made using molecular data from currently living species. With the availability of genomic data from multiple related species, molecular evolution has become one of the most active and fastest growing fields of study in genomics and bioinformatics.
Most studies in molecular evolution rely heavily on statistical procedures based on stochastic process modelling and advanced computational methods including high-dimensional numerical optimization and Markov Chain Monte Carlo. This book provides an overview of the statistical theory and methods used in studies of molecular evolution. It includes an introductory section suitable for readers that are new to the field, a section discussing practical methods for data analysis, and more specialized sections discussing specific models and addressing statistical issues relating to estimation and model choice. The chapters are written by the leaders of field and they will take the reader from basic introductory material to the state-of-the-art statistical methods.
This book is suitable for statisticians seeking to learn more about applications in molecular evolution and molecular evolutionary biologists with an interest in learning more about the theory behind the statistical methods applied in the field. The chapters of the book assume no advanced mathematical skills beyond basic calculus, although familiarity with basic probability theory will help the reader. Most relevant statistical concepts are introduced in the book in the context of their application in molecular evolution, and the book should be accessible for most biology graduate students with an interest in quantitative methods and theory.
Rasmus Nielsen received his Ph.D. form the University of California at Berkeley in 1998 and after a postdoc at Harvard University, he assumed a faculty position in Statistical Genomics at Cornell University. He is currently an Ole Rømer Fellow at the University of Copenhagen and holds a Sloan Research Fellowship. His is an associate editor of the Journal of Molecular Evolution and has published more than fifty original papers in peer-reviewed journals on the topic of this book.
From the reviews:
"...Overall this is a very useful book in an area of increasing importance." Journal of the Royal Statistical Society
"I find Statistical Methods in Molecular Evolution very interesting and useful. It delves into problems that were considered very difficult just several years ago...the book is likely to stimulate the interest of statisticians that are unaware of this exciting field of applications. It is my hope that it will also help the 'wet lab' molecular evolutionist to better understand mathematical and statistical methods." Marek Kimmel for the Journal of the American Statistical Association, September 2006
"Who should read this book? We suggest that anyone who deals with molecular data (who does not?) and anyone who asks evolutionary questions (who should not?) ought to consult the relevant chapters in this book." Dan Graur and Dror Berel for Biometrics, September 2006
"Coalescence theory facilitates the merger of population genetics theory with phylogenetic approaches, but still, there are mostly two camps: phylogeneticists and population geneticists. Only a few people are moving freely between them. Rasmus Nielsen is certainly one of these researchers, and his work so far has merged many population genetic and phylogenetic aspects of biological research under the umbrella of molecular evolution. Although Nielsen did not contribute a chapter to his book, his work permeates all its chapters. This book gives an overview of his interests and current achievements in molecular evolution. In short, this book should be on your bookshelf." Peter Beerli for Evolution, 60(2), 2006
| | Chemoinformatics in Drug Discovery (Methods and Principles in Medicinal Chemistry) By
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Book Description:
This handbook provides the first-ever inside view of today's integrated approach to rational drug design. Chemoinformatics experts from large pharmaceutical companies, as well as from chemoinformatics service providers and from academia demonstrate what can be achieved today by harnessing the power of computational methods for the drug discovery process.
With the user rather than the developer of chemoinformatics software in mind, this book describes the successful application of computational tools to real-life problems and presents solution strategies to commonly encountered problems. It shows how almost every step of the drug discovery pipeline can be optimized and accelerated by using chemoinformatics tools—from the management of compound databases to targeted combinatorial synthesis, virtual screening and efficient hit-to-lead transition.
An invaluable resource for drug developers and medicinal chemists in academia and industry.
| | Research in Computational Molecular Biology: 11th Annunal International Conference, RECOMB 2007Oakland, CA, USA, April 21-25, 2007Proceedings (Lecture Notes in Computer Science) By
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Book Description:
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 presented were carefully reviewed and selected from just under 170 submissions. As the top conference in computational molecular biology, RECOMB addresses all current issues in algorithmic, theoretical, and experimental bioinformatics.
