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Computational Intelligence in Biomedicine and Bioinformatics

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Cover of 'Computational Intelligence in Biomedicine and Bioinformatics'

Table of Contents

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    Book Overview
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    Chapter 1 Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges
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    Chapter 2 Data Mining and Genetic Algorithms: Finding Hidden Meaning in Biological and Biomedical Data
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    Chapter 3 The Use of Rough Sets as a Data Mining Tool for Experimental Bio-data
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    Chapter 4 Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and Bioinformatics Decision Support
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    Chapter 5 Data-Mining of Time-Domain Features from Neural Extracellular Field Data
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    Chapter 6 Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers
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    Chapter 7 Computational Intelligence Techniques in Image Segmentation for Cytopathology
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    Chapter 8 Curvature Flow Based 3D Surface Evolution Model for Polyp Detection and Visualization in CT Colonography
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    Chapter 9 Assisting Cancer Diagnosis with Fuzzy Neural Networks
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    Chapter 10 Computational Intelligence in Clinical Oncology: Lessons Learned from an Analysis of a Clinical Study
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    Chapter 11 Artificial Immune Systems in Bioinformatics
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    Chapter 12 Evolutionary Algorithms for the Protein Folding Problem: A Review and Current Trends
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    Chapter 13 Computational Intelligence in Biomedicine and Bioinformatics
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    Chapter 14 Considering Stem-Loops as Sequence Signals for Finding Ribosomal RNA Genes
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    Chapter 15 Power-Law Signatures and Patchiness in Genechip Oligonucleotide Microarrays
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    Chapter 16 Case Study: Structure and Function Prediction of a Protein with No Functionally Characterized Homolog
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    Chapter 17 From Biomedical Literature to Knowledge: Mining Protein-Protein Interactions
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Title
Computational Intelligence in Biomedicine and Bioinformatics
Published by
Springer Berlin Heidelberg, January 2009
DOI 10.1007/978-3-540-70778-3
ISBNs
978-3-54-070776-9, 978-3-54-070778-3
Editors

Smolinski, Tomasz G., Milanova, Mariofanna G., Hassanien, Aboul-Ella

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Egypt 1 10%
Unknown 9 90%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 30%
Professor 2 20%
Student > Ph. D. Student 2 20%
Other 1 10%
Researcher 1 10%
Other 2 20%
Readers by discipline Count As %
Computer Science 3 30%
Unspecified 2 20%
Mathematics 2 20%
Agricultural and Biological Sciences 2 20%
Biochemistry, Genetics and Molecular Biology 1 10%
Other 1 10%