Ebook: Bioinformatics Algorithms: Techniques and Applications

Серьёзная книга о биоинформатических алгоритмах.
Contents
1 Educating Biologists in the 21st Century: Bioinformatics Scientists versus Bioinformatics Technicians
2 Dynamic Programming Algorithms for Biological Sequence and Structure Comparison
3 Graph Theoretical Approaches to Delineate Dynamics of Biological Processes
4 Advances in Hidden Markov Models for Sequence Annotation
5 Sorting- and FFT-Based Techniques in the Discovery of Biopatterns
6 A Survey of Seeding for Sequence Alignmen
7 The Comparison of Phylogenetic Networks: Algorithms and Complexity
8 Formal Models of Gene Clusters
9 Integer Linear Programming Techniques for Discovering Approximate Gene Clusters
10 Efficient Combinatorial Algorithms for DNA Sequence Processing
11 Algorithms for Multiplex PCR Primer Set Selection with Amplification Length Constraints
12 Recent Developments in Alignment and Motif Finding for Sequences and Networks
13 Algorithms for Oligonucleotide Microarray Layout
14 Classification Accuracy Based Microarray Missing Value Imputation
15 Meta-Analysis of Microarray Data
16 Phasing Genotypes Using a Hidden Markov Model
17 Analytical and Algorithmic Methods for Haplotype Frequency Inference: What Do They Tell Us?
18 Optimization Methods for Genotype Data Analysis in Epidemiological Studies
19 Topological Indices in Combinatorial Chemistry
20 Efficient Algorithms for Structural Recall in Databases
21 Computational Approaches to Predict Protein–Protein and Domain–Domain Interactions

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Presents algorithmic techniques for solving problems in bioinformatics, including applications that shed new light on molecular biology

This book introduces algorithmic techniques in bioinformatics, emphasizing their application to solving novel problems in post-genomic molecular biology. Beginning with a thought-provoking discussion on the role of algorithms in twenty-first-century bioinformatics education, Bioinformatics Algorithms covers:

• General algorithmic techniques, including dynamic programming, graph-theoretical methods, hidden Markov models, the fast Fourier transform, seeding, and approximation algorithms

• Algorithms and tools for genome and sequence analysis, including formal and approximate models for gene clusters, advanced algorithms for non-overlapping local alignments and genome tilings, multiplex PCR primer set selection, and sequence/network motif finding

• Microarray design and analysis, including algorithms for microarray physical design, missing value imputation, and meta-analysis of gene expression data

• Algorithmic issues arising in the analysis of genetic variation across human population, including computational inference of haplotypes from genotype data and disease association search in case/control epidemiologic studies

• Algorithmic approaches in structural and systems biology, including topological and structural classification in biochemistry, and prediction of protein-protein and domain-domain interactions

Each chapter begins with a self-contained introduction to a computational problem; continues with a brief review of the existing literature on the subject and an in-depth description of recent algorithmic and methodological developments; and concludes with a brief experimental study and a discussion of open research challenges. This clear and approachable presentation makes the book appropriate for researchers, practitioners, and graduate students alike.