Pattern Discovery in Bioinformatics

Pattern Discovery in Bioinformatics : Theory & Algorithms

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Description

The computational methods of bioinformatics are being used more and more to process the large volume of current biological data. Promoting an understanding of the underlying biology that produces this data, Pattern Discovery in Bioinformatics: Theory and Algorithms provides the tools to study regularities in biological data. Taking a systematic approach to pattern discovery, the book supplies sound mathematical definitions and efficient algorithms to explain vital information about biological data. It explores various data patterns, including strings, clusters, permutations, topology, partial orders, and boolean expressions. Each of these classes captures a different form of regularity in the data, providing possible answers to a wide range of questions. The book also reviews basic statistics, including probability, information theory, and the central limit theorem. This self-contained book provides a solid foundation in computational methods, enabling the solution of difficult biological questions.show more

Product details

  • Hardback | 512 pages
  • 157.48 x 231.14 x 33.02mm | 861.82g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • 95 black & white illustrations
  • 1584885491
  • 9781584885498

Table of contents

INTRODUCTION Ubiquity of Patterns Motivations Form Biology The Need for Rigor Who Is a Reader of This Book? THE FUNDAMENTALS BASIC ALGORITHMICS Introduction Graphs Tree Problem 1: (Minimum Spanning Tree) Tree Problem 2: (Steiner Tree) Tree Problem 3: (Minimum Mutation Labeling) Storing and Retrieving Elements Asymptotic Functions Recurrence Equations NP-Complete Class of Problems BASIC STATISTICS Introduction Basic Probability The Bare Truth about Inferential Statistics Summary WHAT ARE PATTERNS? Introduction Common Thread Pattern Duality Irredundant Patterns Constrained Patterns When Is a Pattern Specification Non-Trivial? Classes of Patterns PATTERNS ON LINEAR STRINGS MODELING THE STREAM OF LIFE Introduction Modeling a Biopolymer Bernoulli Scheme Markov Chain Hidden Markov Model (HMM) Comparison of the Schemes Conclusion STRING PATTERN SPECIFICATIONS Introduction Notation Solid Patterns Rigid Patterns Extensible Patterns Generalizations ALGORITHMS AND PATTERN STATISTICS Introduction Discovery Algorithm Pattern Statistics Rigid Patterns Extensible Patterns Measure of Surprise Applications MOTIF LEARNING Introduction: Local Multiple Alignment Probabilistic Model: Motif Profile The Learning Problem Importance Measure Algorithms to Learn a Motif Profile An Expectation Maximization Framework A Gibbs Sampling Strategy Interpreting the Motif Profile in Terms of p THE SUBTLE MOTIF Introduction: Consensus Motif Combinatorial Model: Subtle Motif Distance between Motifs Statistics of Subtle Motifs Performance Score Enumeration Schemes A Combinatorial Algorithm A Probabilistic Algorithm A Modular Solution Conclusion PATTERNS ON META-DATA PERMUTATION PATTERNS Introduction Notation How Many Permutation Patterns? Maximality Parikh Mapping-Based Algorithm Intervals Intervals to PQ Trees Applications Conclusion PERMUTATION PATTERN PROBABILITIES Introduction Unstructured Permutations Structured Permutations TOPOLOGICAL MOTIFS Introduction What Are Topological Motifs? The Topological Motif Compact Topological Motifs The Discovery Method Related Classical Problems Applications Conclusion SET-THEORETIC ALGORITHMIC TOOLS Introduction Some Basic Properties of Finite Sets Partial Order Graph G(S,E) of Sets Boolean Closure of Sets Consecutive (Linear) Arrangement of Set Members Maximal Set Intersection Problem (maxSIP) Minimal Set Intersection Problem (minSIP) Multi-Sets Adapting the Enumeration Scheme EXPRESSION AND PARTIAL ORDER MOTIFS Introduction Extracting (monotone CNF) Boolean Expressions Extracting Partial Orders Statistics of Partial Orders Redescriptions Application: Partial Order of Expressions Summary REFERENCES INDEX Exercises appear at the end of every chapter.show more

About Laxmi Parida

IBM TJ Watson Research Center, Yorktown Heights, New York, Ushow more