Recent advances in high-throughput technologies have resulted in
a deluge of biological information. Yet the storage, analysis, and
interpretation of such multifaceted data require effective and
efficient computational tools.
This unique text/reference addresses the need for a unified
framework describing how soft computing and machine learning
techniques can be judiciously formulated and used in building
efficient pattern recognition models. The book reviews both
established and cutting-edge research, following a clear structure
reflecting the major phases of a pattern recognition system:
classification, feature selection, and clustering. The text
provides a careful balance of theory, algorithms, and applications,
with a particular emphasis given to applications in computational
biology and bioinformatics.
Topics and features: reviews the development of scalable pattern
recognition algorithms for computational biology and
bioinformatics; integrates different soft computing and machine
learning methodologies with pattern recognition tasks; discusses in
detail the integration of different techniques for handling
uncertainties in decision-making and efficiently mining large
biological datasets; presents a particular emphasis on real-life
applications, such as microarray expression datasets and magnetic
resonance images; includes numerous examples and experimental
results to support the theoretical concepts described; concludes
each chapter with directions for future research and a
comprehensive bibliography.
This important work will be of great use to graduate students
and researchers in the fields of computer science, electrical and
biomedical engineering. Researchers and practitioners involved in
pattern recognition, machine learning, computational biology and
bioinformatics, data mining, and soft computing will also find the
book invaluable.
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