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HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In.

Zustand: New. pp. xviii + 456 Illus.

Gebunden. Zustand: New. Yan-Qing Zhang, PhD, is an Associate Professor of Computer Science at the Georgia State University, Atlanta. His research interests include hybrid intelligent systems, neural networks, fuzzy logic, evolutionary computation, Yin-Yang computation, granular co.

Zustand: New. Machine learning techniques such as Markov models, support vector machines, neural networks, graphical models, etc. , have been successful in analyzing life science data because of their capabilities of handling randomness and uncertainties of data and noise and in generalization. Series: Wiley Series in Bioinformatics. Num Pages: 456 pages, Illustrations. BIC Classification: PBW; PSAK; TJ. Category: (P) Professional & Vocational. Dimension: 237 x 164 x 27. Weight in Grams: 788. . 2008. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Zustand: Brand New. 1st edition. 456 pages. 9.29x6.22x1.02 inches. In Stock.

Buch. Zustand: Neu. Neuware - An introduction to machine learning methods and their applications to problems in bioinformaticsMachine 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.