Computational Intelligence in Biomedicine and Bioinformatics [electronic resource] : Current Trends and Applications / edited by Tomasz G. Smolinski, Mariofanna G. Milanova, Aboul-Ella Hassanien.Material type: TextLanguage: English Series: Studies in Computational Intelligence: 151Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008Description: XXIV, 432 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540707783Subject(s): Engineering | Medicine | Artificial intelligence | Bioinformatics | Biology -- Data processing | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics) | Biomedicine general | Bioinformatics | Computer Appl. in Life SciencesAdditional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
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.
The purpose of this book is to provide an overview of powerful state-of-the-art methodologies that are currently utilized for biomedicine and/ or bioinformatics-oriented applications, so that researchers working in those fields could learn of new methods to help them tackle their problems. On the other hand, the CI community will find this book useful by discovering a new and intriguing area of applications. In order to help fill the gap between the scientists on both sides of this spectrum, the editors have solicited contributions from researchers actively applying computational intelligence techniques to important problems in biomedicine and bioinformatics. The book is divided into three major parts. Part I, Techniques and Methodologies, contains a selection of contributions that provide a review of several theories and methods that could be (or to some extent already are) of great benefit to practitioners in the fields of biomedicine and bioinformatics dealing with problems of data exploration and mining, search-space exploration, optimization, etc. Part II of this book, Computational Intelligence in Biomedicine, contains a collection of contributions on current state-of-the-art biomedical applications of CI in clinical oncology, neurology, pathology, and proteomics. Part II, Computational Intelligence in Biomedicine, contains a collection of chapters treating on applications of CI methods to solving bioinformatics problems including protein structure and function prediction, protein folding, finding ribosomal RNA genes, and microarray analysis.