Buch, Englisch, Band 176, 211 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1090 g
Buch, Englisch, Band 176, 211 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1090 g
Reihe: Studies in Fuzziness and Soft Computing
ISBN: 978-3-540-22901-8
Verlag: Springer Berlin Heidelberg
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
- Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Biotechnologie Industrielle Biotechnologie
- Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
- Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
- Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
Weitere Infos & Material
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.
