Machine Learning in Bioinformatics

Autor: Yanqing Zhang, Jagath C. Rajapakse

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Legare: Copertă tare

Editura: John Wiley & Sons Inc

Disponibilitate: În depozitul extern

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An introduction to machine learning methods and their applications to problems in bioinformatics Mac...

Informații despre carte

Autor

Yanqing Zhang, Jagath C. Rajapakse

Limbă

engleză

Legare

Carte - Copertă tare

Publicat

2008

Pagini

456

EAN

9780470116623

ISBN

0470116625

Enbook ID

04880882

Editura

John Wiley & Sons Inc

Greutate

788

Dimensiuni

164 x 237 x 27

Descriere completă

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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