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.
General
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