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.
