Structural Bioinformatics

Structural Bioinformatics : An Algorithmic Approach

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The Beauty of Protein Structures and the Mathematics behind Structural Bioinformatics Providing the framework for a one-semester undergraduate course, Structural Bioinformatics: An Algorithmic Approach shows how to apply key algorithms to solve problems related to macromolecular structure. Helps Students Go Further in Their Study of Structural Biology Following some introductory material in the first few chapters, the text solves the longest common subsequence problem using dynamic programming and explains the science models for the Nussinov and MFOLD algorithms. It then reviews sequence alignment, along with the basic mathematical calculations needed for measuring the geometric properties of macromolecules. After looking at how coordinate transformations facilitate the translation and rotation of molecules in a 3D space, the author introduces structural comparison techniques, superposition algorithms, and algorithms that compare relationships within a protein. The final chapter explores how regression and classification are becoming more useful in protein analysis and drug design. At the Crossroads of Biology, Mathematics, and Computer Science Connecting biology, mathematics, and computer science, this practical text presents various bioinformatics topics and problems within a scientific methodology that emphasizes nature (the source of empirical observations), science (the mathematical modeling of the natural process), and computation (the science of calculating predictions and mathematical objects based on mathematical models).show more

Product details

  • Hardback | 429 pages
  • 158 x 238 x 28mm | 762.03g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New.
  • 124 black & white illustrations, 24 colour illustrations, 6 black & white tables
  • 1584886838
  • 9781584886839

Review quote

... the book presents a number of topics in structural bioinformatics, aiming to emphasize the beauty of the area as well as some of the main problems. It targets advanced undergraduate students and hence the description of more complicated algorithms is avoided. It nevertheless provides an interesting introduction to the area. -Lucian Ilie, Mathematical Reviews, Issue 2009kshow more

Table of contents

Preface The Study of Structural Bioinformatics Motivation Small Beginnings Structural Bioinformatics and the Scientific Method A More Detailed Problem Analysis: Force Fields Modeling Issues Sources of Error Summary Introduction to Macromolecular Structure Motivation Overview of Protein Structure Overview of RNA Structure Data Sources, Formats, and Applications Motivation Sources of Structural Data PDB File Format Visualization of Molecular Data Software for Structural Bioinformatics Dynamic Programming Motivation Introduction A DP Example: The Al Gore Rhythm for Giving Talks A Recipe for Dynamic Programming Longest Common Subsequence RNA Secondary Structure Prediction Motivation Introduction to the Problem The Nussinov Dynamic Programming The MFOLD Algorithm: Terminology Protein Sequence Alignment Protein Homology Variations in the Global Alignment Algorithm The Significance of a Global Alignment Local Alignment Protein Geometry Introduction Calculations Related to Protein Geometry Ramachandran Plots Inertial Axes Coordinate Transformations Motivation Introduction Translation Transformations Rotation Transformations Isometric Transformations Structure Comparison, Alignment, and Superposition Motivation Introduction Techniques for Structural Comparison Scoring Similarities and Optimizing Scores Superposition Algorithms Algorithms Comparing Relationships within a Protein Machine Learning Motivation Issues of Complexity Prediction via Machine Learning Data Used during Training and Testing Objectives of the Learning Algorithm Linear Regression Ridge Regression Preamble for Kernel Methods Kernel Functions Classification Heuristics for Classification Nearest Neighbor Classification Support Vector Machines Linearly Nonseparable Data Support Vector Machines and Kernels Expected Test Error Transparency Overview of the Appendices Indexshow more

About Forbes J. Burkowski

University of Waterloo, Ontario, Canadashow more

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