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Applied Algorithms for Bioinformatics Problems

With significant growth of bio-molecular sequence data in the last decade the need for algorithms to extract patterns and meaningful;

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Applied Bioinformatics

At last, here is a baseline book for anyone who is confused by cryptic computer programs, algorithms and formulae, but wants to learn about;

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Bioinformatics Algorithms

of bioinformatics applications that demonstrate the main principles underlying real world applications. Presents an ideal text for;

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Algorithms in Bioinformatics

teaching material, Algorithms in Bioinformatics: A Practical Introduction provides an in-depth introduction to the algorithmic techniques applied;

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Algorithms in Bioinformatics

teaching material, Algorithms in Bioinformatics: A Practical Introduction provides an in-depth introduction to the algorithmic techniques applied;

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Handbook Of Applied Algorithms

their comprehension of the material presented, Handbook of Applied Algorithms is a much-needed resource for researchers, practitioners, and;

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An Introduction to Bioinformatics Algorithms

or more solutions for each; and brief biographical sketches of leading figures in the field. These interesting vignettes offer students a;

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Basics of Bioinformatics

Research, Association Analysis for Human Diseases: Methods and Examples, Data Mining and Knowledge Discovery Methods with Case Examples, Applied;

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Bioinformatics of Genome Regulation and Structure

- The data gathered can be used to solve a wide range of problems - for basic science and applied science;

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Problem Solving Handbook in Computational Biology and Bioinformatics

solutions to well-abstracted problems as building blocks to solve larger scope problems. Problem Solving Handbook for Computational Biology and;

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Handbook of Chemoinformatics Algorithms

, the book presents algorithms for two-dimensional chemical structures and three-dimensional representations of molecules. It then focuses on;

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The Problem Solving Handbook for Computational Biology and Bioinformatics

solutions to well-abstracted problems as building blocks to solve larger scope problems. Problem Solving Handbook for Computational Biology and;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Bayesian Modeling in Bioinformatics

Bayesian Modeling in Bioinformatics discusses the development and application of Bayesian statistical methods for the analysis of high;

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Algorithms in Bioinformatics

, WAOA, IWPEC, and ATMOS. WABI 2005 was sponsored by EATCS (the European Assoc- tion for Theoretical Computer Science), the ISCB (the;

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Basic Applied Bioinformatics

An accessible guide that introduces students in all areas of life sciences to bioinformatics Basic Applied Bioinformatics provides a;

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Optimization in Solving Elliptic Problems

for the arising grid systems, asymptotically optimal iterative methods can be applied. Optimization in Solving Elliptic Problems describes;

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Elements of the General Theory of Optimal Algorithms

in construction of T-efficient algorithms for computing -solutions to problems of computational and applied mathematics, is also explored. The readership;

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Bioinformatics Challenges at the Interface of Biology and Computer Science

. The book considers how the need for biological databases drove the evolution of bioinformatics; it reviews bioinformatics basics (including;

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Algorithms in Bioinformatics

This book constitutes the refereed proceedings of the 11th International Workshop on Algorithms in B;...

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Information Systems Design and Intelligent Applications

, evolutionary algorithms, and bio-inspired algorithms, are currently being applied to solve challenging problems in various domains.;

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Optimization Techniques for Solving Complex Problems

Real-world problems and modern optimization techniques to solve them Here, a team of international experts brings together core ideas for;

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Metaheuristics

engineering design, routing, and scheduling. It presents the main design questions for all families of metaheuristics and clearly illustrates how to;

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Self-Learning and Adaptive Algorithms for Business Applications

In today's data-driven world, more sophisticated algorithms for data processing are in high demand, mainly when the data cannot be handled;

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Simulation Algorithms for Computational Systems Biology

applied to a set of modeling cases. Starting from basic simulation algorithms, the book also introduces more advanced techniques that support;

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Guide to Graph Algorithms

algorithms, approaches for NP-hard graph problems, and approximation algorithms and heuristics for such problems. The work also provides a;

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Immunological Bioinformatics

they are applied to immunology, including a succinct account of the main biological concepts for students and researchers with backgrounds;

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