Ebook: Bioinformatics Using Computational Intelligence Paradigms
- Tags: Numerical and Computational Methods in Engineering, Appl.Mathematics/Computational Methods of Engineering, Bioinformatics, Artificial Intelligence (incl. Robotics), Biotechnology, Mathematical Biology in General
- Series: Studies in Fuzziness and Soft Computing 176
- Year: 2005
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
Bioinformatics as well as 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 a promising cross-fertilisation and interdisciplinary work between these growing fields.
Bioinformatics as well as 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 a promising cross-fertilisation and interdisciplinary work between these growing fields.
Bioinformatics as well as 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 a promising cross-fertilisation and interdisciplinary work between these growing fields.
Content:
Front Matter....Pages -
Medical Bioinformatics: Detecting Molecular Diseases with Case-Based Reasoning....Pages 1-23
Prototype Based Recognition of Splice Sites....Pages 25-55
Content Based Image Compression in Biomedical High-Throughput Screening Using Artificial Neural Networks....Pages 57-73
Discriminative Clustering of Yeast Stress Response....Pages 75-91
A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle....Pages 93-117
Class Prediction with Microarray Datasets....Pages 119-141
Random Voronoi Ensembles for Gene Selection in DNA Microarray Data....Pages 143-166
Cancer Classification with Microarray Data Using Support Vector Machines....Pages 167-189
Artificial Neural Networks for Reducing the Dimensionality of Gene Expression Data....Pages 191-211
Bioinformatics as well as 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 a promising cross-fertilisation and interdisciplinary work between these growing fields.
Content:
Front Matter....Pages -
Medical Bioinformatics: Detecting Molecular Diseases with Case-Based Reasoning....Pages 1-23
Prototype Based Recognition of Splice Sites....Pages 25-55
Content Based Image Compression in Biomedical High-Throughput Screening Using Artificial Neural Networks....Pages 57-73
Discriminative Clustering of Yeast Stress Response....Pages 75-91
A Dynamic Model of Gene Regulatory Networks Based on Inertia Principle....Pages 93-117
Class Prediction with Microarray Datasets....Pages 119-141
Random Voronoi Ensembles for Gene Selection in DNA Microarray Data....Pages 143-166
Cancer Classification with Microarray Data Using Support Vector Machines....Pages 167-189
Artificial Neural Networks for Reducing the Dimensionality of Gene Expression Data....Pages 191-211
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