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This treatise presents an integrated perspective on the interplay
of set theory and graph theory, providing an extensive selection of
examples that highlight how methods from one theory can be used to
better solve problems originated in the other. Features: explores
the interrelationships between sets and graphs and their
applications to finite combinatorics; introduces the fundamental
graph-theoretical notions from the standpoint of both set theory
and dyadic logic, and presents a discussion on set universes;
explains how sets can conveniently model graphs, discussing set
graphs and set-theoretic representations of claw-free graphs;
investigates when it is convenient to represent sets by graphs,
covering counting and encoding problems, the random generation of
sets, and the analysis of infinite sets; presents excerpts of
formal proofs concerning graphs, whose correctness was verified by
means of an automated proof-assistant; contains numerous exercises,
examples, definitions, problems and insight panels.
This treatise presents an integrated perspective on the interplay
of set theory and graph theory, providing an extensive selection of
examples that highlight how methods from one theory can be used to
better solve problems originated in the other. Features: explores
the interrelationships between sets and graphs and their
applications to finite combinatorics; introduces the fundamental
graph-theoretical notions from the standpoint of both set theory
and dyadic logic, and presents a discussion on set universes;
explains how sets can conveniently model graphs, discussing set
graphs and set-theoretic representations of claw-free graphs;
investigates when it is convenient to represent sets by graphs,
covering counting and encoding problems, the random generation of
sets, and the analysis of infinite sets; presents excerpts of
formal proofs concerning graphs, whose correctness was verified by
means of an automated proof-assistant; contains numerous exercises,
examples, definitions, problems and insight panels.
Presenting the fundamental algorithms and data structures that
power bioinformatics workflows, this book covers a range of topics
from the foundations of sequence analysis (alignments and hidden
Markov models) to classical index structures (k-mer indexes, suffix
arrays, and suffix trees), Burrows–Wheeler indexes, graph
algorithms, network flows, and a number of advanced omics
applications. The chapters feature numerous examples, algorithm
visualizations, and exercises, providing graduate students,
researchers, and practitioners with a powerful algorithmic toolkit
for the applications of high-throughput sequencing. An accompanying
website (www.genome-scale.info) offers supporting teaching
material. The second edition strengthens the toolkit by covering
minimizers and other advanced data structures and their use in
emerging pangenomics approaches.
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(1)
R487
Discovery Miles 4 870
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