Bioinformatics Using Computational Intelligence Paradigms / Edition 1

Bioinformatics Using Computational Intelligence Paradigms / Edition 1

ISBN-10:
3642061737
ISBN-13:
9783642061738
Pub. Date:
12/15/2010
Publisher:
Springer Berlin Heidelberg
ISBN-10:
3642061737
ISBN-13:
9783642061738
Pub. Date:
12/15/2010
Publisher:
Springer Berlin Heidelberg
Bioinformatics Using Computational Intelligence Paradigms / Edition 1

Bioinformatics Using Computational Intelligence Paradigms / Edition 1

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Overview

Bioinformatics and computational intelligence are undoubtedly remarkably fast growing fields of research and real-world applications with enormous potential for current and future developments. Bioinformatics Using Computational Intelligence Paradigms contains recent theoretical approaches and guiding applications of biologically inspired information processing systems (computational intelligence) against the background of bioinformatics. This carefully edited monograph combines the latest results of bioinformatics and computational intelligence, and offers promising cross-fertilization and interdisciplinary work between these growing fields.


Product Details

ISBN-13: 9783642061738
Publisher: Springer Berlin Heidelberg
Publication date: 12/15/2010
Series: Studies in Fuzziness and Soft Computing , #176
Edition description: Softcover reprint of hardcover 1st ed. 2005
Pages: 211
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Medical Bioinformatics: Detecting Molecular Diseases with Case-Based Reasoning.- Prototype Based Recognition of Splice Sites.- Contact Based Image Compression in Biomedical High-Throughput Screening Using Artificial Neural Networks.- Discriminative Clustering of Yeast Stress Response.- A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle.- Class Prediction with Microarray Datasets.- Random Voronoi Ensembles for Gene Selection in DNA Microarray Data.- Cancer Classification with Microarray Data Using Support Vector Machines.- Artificial Neural Networks for Reducing the Dimensionality of Gene Expression Data.
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