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Machine Learning in Bioinformatics (Hardcover)
Loot Price: R3,647
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Machine Learning in Bioinformatics (Hardcover)
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An introduction to machine learning methods and their applications
to problems in bioinformatics Machine learning techniques are
increasingly being used to address problems in computational
biology and bioinformatics. Novel computational techniques to
analyze high throughput data in the form of sequences, gene and
protein expressions, pathways, and images are becoming vital for
understanding diseases and future drug discovery. Machine learning
techniques such as Markov models, support vector machines, neural
networks, and graphical models have been successful in analyzing
life science data because of their capabilities in handling
randomness and uncertainty of data noise and in generalization.
From an internationally recognized panel of prominent researchers
in the field, Machine Learning in Bioinformatics compiles recent
approaches in machine learning methods and their applications in
addressing contemporary problems in bioinformatics. Coverage
includes: feature selection for genomic and proteomic data mining;
comparing variable selection methods in gene selection and
classification of microarray data; fuzzy gene mining;
sequence-based prediction of residue-level properties in proteins;
probabilistic methods for long-range features in biosequences; and
much more. Machine Learning in Bioinformatics is an indispensable
resource for computer scientists, engineers, biologists,
mathematicians, researchers, clinicians, physicians, and medical
informaticists. It is also a valuable reference text for computer
science, engineering, and biology courses at the upper
undergraduate and graduate levels.
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