U. Seiffert / L.C. Jain / P. Schweizer (eds.)

Bioinformatics Using Computational Intelligence Paradigms

Herausgegeben:Seiffert, Udo; Schweizer, Patric

• Gebundenes Buch

Produktbeschreibung

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.

Produktdetails

• Studies in Fuzziness and Soft Computing 176
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 10950913, 978-3-540-22901-8
• 2005
• Seitenzahl: 224
• Erscheinungstermin: 17. Januar 2005
• Englisch
• Abmessung: 241mm x 160mm x 17mm
• Gewicht: 446g
• ISBN-13: 9783540229018
• ISBN-10: 3540229019
• Artikelnr.: 13923256

Autorenporträt

U. Seiffert, Leibniz-Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany / L.C. Jain, University of Southern Australia, Adelaide, SA, Australia / P. Schweizer, Leibniz-Institute of Plant Genetics and Crop Plant Research, Gatersleben, Germany

Inhaltsangabe

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.