Bioinformatics Using Computational Intelligence Paradigms / Edition 1

Bioinformatics Using Computational Intelligence Paradigms / Edition 1

ISBN-10:
3540229019
ISBN-13:
9783540229018
Pub. Date:
03/14/2005
Publisher:
Springer Berlin Heidelberg
ISBN-10:
3540229019
ISBN-13:
9783540229018
Pub. Date:
03/14/2005
Publisher:
Springer Berlin Heidelberg
Bioinformatics Using Computational Intelligence Paradigms / Edition 1

Bioinformatics Using Computational Intelligence Paradigms / Edition 1

$109.99 Current price is , Original price is $109.99. You
$109.99 
  • SHIP THIS ITEM
    Qualifies for Free Shipping
  • PICK UP IN STORE
    Check Availability at Nearby Stores
  • SHIP THIS ITEM

    Temporarily Out of Stock Online

    Please check back later for updated availability.


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: 9783540229018
Publisher: Springer Berlin Heidelberg
Publication date: 03/14/2005
Series: Studies in Fuzziness and Soft Computing , #176
Edition description: 2005
Pages: 211
Product dimensions: 6.10(w) x 9.25(h) x 0.36(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.
From the B&N Reads Blog

Customer Reviews