Bioinformatics

Problem Solving Paradigms

By: Volker Sperschneider

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Hardcover | 14 August 2008

At a Glance

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308 Pages

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23.5 x 15.5 x 2.54

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What is this book good for? Imagine you are a computer scientist working in the bioinformatics area. P- bably you will be a member of a highly interdisciplinary team consisting of biologists, chemists, mathematicians, computer scientists ranging from p- grammers to algorithm engineers, and eventually people from various further ?elds. A major problem within such interdisciplinary teams is always to ?nd some common language, and, for each member of some discipline, to have profound knowledge of what are the notions, basic concepts and goals of the other participating disciplines, as well as of what they can contribute to the solutionofonesownproblems. Thisdoes,ofcourse,notmeanthatacomputer scientist should do the job of the biologist. Nevertheless, a computer scientist should be able to understand what a biologist deals with. On the other hand, the biologist should not do the computer scientists job, but should know what computer science and algorithm engineering might contribute to the solution of her/his problems, and also how problems should be stated in order for the computer scientist to understand them. This book primarily aims to show the potential that algorithm engin- ring o?ers for the solution of core bioinformatics problems.

Industry Reviews

From the reviews:

"Computer science (CS) and molecular biology meet in the bioinformatics field. ... this book presents a CS perspective of the problems involved and considers some of the fundamental data structures and algorithms required for solving them. ... This book is appropriate for biologists who seek an overview of ways in which a computer scientist might approach the problems involved." (Jeffrey Putnam, ACM Computing Reviews, May, 2009)

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Non-Fiction Computing & I.T.Computer Programming & Software Development Algorithms & Data Structures Computer Science Science Biology, Life Sciences Biochemistry Life Sciences in General

Core Bioinformatics Problems	p. 1
DNA Mapping and Sequencing	p. 1
Size of Human DNA	p. 1
Copying DNA: Polymerase Chain Reaction (PCR)	p. 2
Hybridization and Microarrays	p. 2
Cutting DNA into Fragments	p. 2
Prefix Cutting	p. 3
Unique Markers	p. 3
Measuring the Length of DNA Molecules	p. 4
Sequencing Short DNA Molecules	p. 4
Mapping Long DNA Molecules	p. 4
Mapping by Single Digestion	p. 5
Mapping by Double Digestion	p. 6
Mid-sized DNA Molecules: Shotgun Sequencing	p. 7
String Storage and Pattern Matching	p. 8
Complexity of Pattern Matching in Bioinformatics	p. 8
First Look at Suffix Trees	p. 9
A Couple of Suffix Tree Applications	p. 10
String Alignment	p. 11
Multiple Alignment	p. 13
Gene Detection	p. 15
Gene Comparison	p. 16
RNA Structure Prediction	p. 18
Protein Structure Prediction	p. 19
Holy Grail Protein Structure Prediction	p. 19
Secondary Structure	p. 20
Tertiary Structure Prediction with Contact Maps	p. 20
HP-Model Approach	p. 21
Protein Threading	p. 22
Molecular Networks	p. 23
Bibliographic Remarks	p. 24
Turning to Algorithmic Problems	p. 25
Presentation of Algorithmic Problems	p. 25
DNA Mapping	p. 26
Mapping by Hybridization	p. 26
Mapping by Single Digestion	p. 27
Mapping by Double Digestion	p. 30
Shotgun Sequencing and Shortest Common Superstrings	p. 31
Exact Pattern Matching	p. 35
Naive Pattern Matching	p. 35
Knuth-Morris-Pratt Algorithm	p. 35
Multi-Pattern Search	p. 37
Multi-Pattern Search	p. 37
Formal Definition of Suffix Trees	p. 38
Soft Pattern Matching = Alignment	p. 41
Alignments Restated	p. 41
Evaluating Alignments by Scoring Functions	p. 41
Multiple Alignment	p. 43
Sum-of-Pairs Maximization	p. 43
Multiple Alignment Along a Guide Tree	p. 43
Consensus Line Optimization	p. 45
Steiner String	p. 45
Equivalence of Consensus Lines and Steiner Strings	p. 45
Generalization to Steiner Trees/Phylogenetic Alignment	p. 47
Profile Alignment	p. 47
Hidden Markov Multiple Alignment	p. 48
Example Heuristic for Obtaining Multiple Alignments	p. 51
Gene Detection	p. 52
Genome Rearrangement	p. 53
RNA Structure Prediction	p. 55
Protein Structure Prediction	p. 56
Comparative Approaches Using Neural Networks	p. 56
Protein Threading	p. 57
Bi-Clustering	p. 59
Bibliographic Remarks	p. 60
Dynamic Programming	p. 61
General Principle of Dynamic Programming	p. 61
Alignment	p. 63
Problem Restated	p. 63
Parameterization	p. 64
Bellman Principle	p. 64
Recursive Solution	p. 64
Number of Different Subcalls and Overall Complexity	p. 65
Tabular Organization of the Bottom-Up Computation	p. 65
Multiple Alignment	p. 67
Affine Gap Alignment	p. 67
Problem Restated	p. 67
Parameterization and Conditioning	p. 68
Bellman Principle	p. 68
Recursive Solution	p. 68
Number of Different Subcalls and Overall Complexity	p. 69
Exon Assembly	p. 69
Problem Restated	p. 69
Parameterization and Conditioning	p. 70
Bellman Principle	p. 70
Recursive Solution	p. 71
Number of Different Subcalls and Overall Complexity	p. 72
Preprocessing of Simple Terms	p. 72
Parallel Computation of Recursive Terms	p. 72
RNA Structure Prediction	p. 73
Problem Restated	p. 73
Parameterization and Conditioning	p. 74
Recursive Solution and Bellman Principle	p. 74
Number of Different Subcalls and Overall Complexity	p. 75
Variation: Free Energy Minimization	p. 76
Viterbi Algorithm	p. 83
Problem Restated	p. 83
Parameterization and Conditioning	p. 83
Bellman Principle	p. 83
Recursive Computation	p. 83
Counting and Overall Complexity	p. 84
Baum-Welch Algorithm	p. 84
Problem Motivation	p. 84
A Couple of Important 'Variables'	p. 84
Computing Forward Variables	p. 85
Computing Backward Variables	p. 85
Computing Transition Variables	p. 85
Computing State Variables	p. 85
Model Improvement	p. 86
Expressiveness of Viterbi Equations	p. 86
Lifted Phylogenetic Alignment	p. 91
Problem Restated	p. 91
Parameterization and Conditioning	p. 92
Bellman Principle	p. 92
Recursive Solution	p. 92
Counting and Overall Complexity	p. 93
Preprocessing of Simple Terms	p. 93
Protein Threading	p. 93
Problem Restated	p. 93
Parameterization and Conditioning	p. 94
Bellman Principle	p. 94
Recursive Solution	p. 94
Counting and Overall Complexity	p. 95
Bibliographic Remarks	p. 95
Intelligent Data Structures	p. 97
Representation Matters	p. 97
Intelligent Data Structures Used in Computer Science	p. 97
Covering a Truncated Board	p. 97
Choose Three Bits and Win	p. 98
Check a Proof	p. 99
PQ-Trees	p. 101
Basic Notions	p. 101
Transformation rules for marked PQ-Trees	p. 103
Suffix Trees	p. 111
Outline of Ukkonen's Algorithm and Basic Procedures	p. 111
Insertion Procedure	p. 112
Saving Single Character Insertions	p. 115
Saving Navigation Steps	p. 116
Estimation of the Overall Number of Visited Nodes	p. 117
Ukkonen's Theorem	p. 120
Common Suffix Tree for Several Strings	p. 123
Applications of Suffix Trees	p. 124
Least Common Ancestor	p. 131
Motivation	p. 131
Least Common Ancestor in Full Binary Trees	p. 131
Least Common Ancestor in Arbitrary Trees	p. 135
Signed Genome Rearrangement	p. 144
Reality-Desire Diagrams of Signed Permutations	p. 144
"'Ping-Pong"' of Lower and Upper Bounds	p. 148
Padding RD-Diagrams	p. 159
Sorting Padded Good Components	p. 161
Summary of the Sorting Procedure	p. 164
Bibliographic Remarks	p. 166
NP-Hardness of Core Bioinformatics Problems	p. 167
Getting Familiar with Basic Notions of Complexity Theory	p. 167
Cook's Theorem: Primer NP-Complete Problem	p. 171
"Zoo" of NP-Complete Problems	p. 175
Bridge to Bioinformatics: Shortest Common Super-Sequence	p. 188
NP-Completeness of Core Bioinformatics Problems	p. 198
Multiple Alignment	p. 198
Shortest Common Superstring	p. 201
Double Digest	p. 204
Protein Threading	p. 204
Bi-Clustering	p. 206
Pseudoknot Prediction	p. 209
The Picture of Presented Reductions	p. 213
Further NP-Complete Bioinformatics Problems	p. 213
Bibliographic Remarks	p. 215
Approximation Algorithms	p. 217
Basics of Approximation Algorithms	p. 217
What is an Approximation Algorithm?	p. 217
First Example: Metric Travelling Salesman	p. 217
Lower and Upper Bounding	p. 218
What are Approximation Algorithms good for?	p. 219
Shortest Common Superstring Problem	p. 221
Feasible Lower Bound	p. 221
Cycle Covers Related to Perfect Matchings Problem	p. 222
Computing Maximum Cost Perfect Matchings	p. 223
Cycle Cover Versus Cyclic Covering by Strings	p. 225
Some Combinatorial Statements on Cyclic Covering	p. 226
4-Approximation Algorithm	p. 228
Putting Things Together	p. 229
Steiner String	p. 230
Problem Restated	p. 230
Feasible Lower Bound	p. 230
2-Approximation Algorithm	p. 231
Phylogenetic Alignment	p. 232
Multiple Alignment	p. 234
Feasible Lower Bound	p. 234
2-Approximation Algorithm	p. 235
Sorting Unsigned Genomes	p. 236
Feasible Lower Bound	p. 236
4-Approximation Algorithm	p. 237
2-Approximation Algorithm	p. 239
HP-Model Protein Structure Prediction	p. 243
Feasible Upper Bound	p. 243
Asymptotic 4-Approximation Algorithm	p. 245
Bibliographic Remarks	p. 248
A Selection of Metaheuristics and Various Projects	p. 249
Multi-Layer Perceptron	p. 249
Architecture of Multi-Layer Perceptrons	p. 250
Forward Propagation of Input Signals	p. 251
Input and Output Encoding	p. 251
Backpropagation of Error Signals	p. 252
Tuning Backpropagation	p. 253
Generalization and Overfitting	p. 253
Application to Secondary Structure Prediction	p. 254
Further Applications	p. 254
Support Vector Machines	p. 255
Architecture of Support Vector Machines	p. 255
Margin Maximization	p. 256
Primal Problem	p. 257
Dual Problem	p. 258
Kernel Function Trick	p. 259
Generalization and Over-Fitting	p. 260
Application to Contact Map Prediction	p. 261
Soft Margin Maximization	p. 263
Semi-Supervised Support Vector Machines	p. 264
Hidden Markov Models	p. 265
Architecture of Hidden Markov Models	p. 266
Causes and Effects	p. 266
Bioinformatics Application: CG Islands	p. 268
Further Applications	p. 269
Genetic Algorithms	p. 269
Basic Characteristics of Genetic Algorithms	p. 269
Application to HP-Fold Optimization	p. 271
Further Applications	p. 272
Ant Colony Optimization	p. 272
Basic Characteristics of Ant Colony Algorithms	p. 273
Application to HP-Fold Optimization	p. 275
Further Applications	p. 276
Bibliographic Remarks	p. 276
References	p. 279
Index	p. 285
Table of Contents provided by Ingram. All Rights Reserved.

Bioinformatics

Problem Solving Paradigms

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

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