Main description:

For the past decade or so, Computational Intelligence (CI) has been an - tremely "hot" topic amongst researchers working in the ?elds of biomedicine and bioinformatics. There are many successful applications of CI in such areas ascomputationalgenomics,predictionofgeneexpression,proteinstructure,and protein-protein interactions, modeling of evolution, or neuronal systems mod- ing and analysis. However, there still are many problems in biomedicine and bioinformatics that are in desperate need of advanced and e?cient compu- tional methodologies to deal with tremendous amounts of data so prevalent in those kinds of researchpursuits. Unfortunately, scientists in both these ?elds are very often unaware of the abundance of computational techniques that could be put to use to help them analyze and understand the data underlying their research inquiries. On the other hand, computational intelligence practitioners are often unfamiliar with the particular problems that their algorithms could be successfully applied for. The separation between the two worlds is partially caused by the use of di?erent languages in these two spheres of science, but also by a relatively small number of publications devoted solely to the purpose of facilitating the exchange of new computational algorithms and methodologies on one hand, and the needs of the realms of biomedicine and bioinformatics on the other.
Inordertohelp?llthegapbetweenthescientistsonbothsidesofthisspectrum, wehavesolicitedcontributionsfromresearchersactivelyapplyingcomputational intelligencetechniquestoimportantproblemsinbiomedicineandbioinformatics. The purpose of this book is to provide an overview of powerful state-of-the-art methodologiesthatarecurrentlyutilizedforbiomedicine-and/orbioinformati- orientedapplications,sothatresearchersworkinginthose?eldscouldlearnofnew methodstohelpthemtackletheirproblems. Ontheotherhand,wealsohopethat the CI community will ?nd this book useful by discovering a new and intriguing area of applications.

Contents:

Techniques and Methodologies.- Computational Intelligence in Solving Bioinformatics Problems: Reviews, Perspectives, and Challenges.- Data Mining and Genetic Algorithms: Finding Hidden Meaning in Biological and Biomedical Data.- The Use of Rough Sets as a Data Mining Tool for Experimental Bio-data.- Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and Bioinformatics Decision Support.- Computational Intelligence in Biomedicine.- Data-Mining of Time-Domain Features from Neural Extracellular Field Data.- Analysis of Spectral Data in Clinical Proteomics by Use of Learning Vector Quantizers.- Computational Intelligence Techniques in Image Segmentation for Cytopathology.- Curvature Flow Based 3D Surface Evolution Model for Polyp Detection and Visualization in CT Colonography.- Assisting Cancer Diagnosis with Fuzzy Neural Networks.- Computational Intelligence in Clinical Oncology: Lessons Learned from an Analysis of a Clinical Study.- Computational Intelligence in Bioinformatics.- Artificial Immune Systems in Bioinformatics.- Evolutionary Algorithms for the Protein Folding Problem: A Review and Current Trends.- Flexible Protein Folding by Ant Colony Optimization.- Considering Stem-Loops as Sequence Signals for Finding Ribosomal RNA Genes.- Power-Law Signatures and Patchiness in Genechip Oligonucleotide Microarrays.- Case Study: Structure and Function Prediction of a Protein with No Functionally Characterized Homolog.- From Biomedical Literature to Knowledge: Mining Protein-Protein Interactions.