Download Computational Intelligence in Biomedicine and by Tomasz G. Smolinski, Mariofanna G. Milanova, Aboul-Ella PDF

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

The function of this e-book is to supply an outline of strong cutting-edge methodologies which are at present applied for biomedicine and/ or bioinformatics-oriented functions, in order that researchers operating in these fields may study of latest how you can aid them take on their difficulties. nonetheless, the CI group will locate this publication worthwhile through learning a brand new and fascinating region of purposes. so one can aid fill the distance among the scientists on either side of this spectrum, the editors have solicited contributions from researchers actively utilizing computational intelligence recommendations to special difficulties in biomedicine and bioinformatics.

The booklet is split into 3 significant elements. half I, thoughts and Methodologies, incorporates a choice of contributions that offer a evaluate of a number of theories and strategies which may be (or to some degree already are) of serious profit to practitioners within the fields of biomedicine and bioinformatics facing difficulties of knowledge exploration and mining, search-space exploration, optimization, and so forth. half II of this e-book, Computational Intelligence in Biomedicine, features a number of contributions on present state of the art biomedical purposes of CI in medical oncology, neurology, pathology, and proteomics. half II, Computational Intelligence in Biomedicine, encompasses a selection of chapters treating on purposes of CI how to fixing bioinformatics difficulties together with protein constitution and serve as prediction, protein folding, discovering ribosomal RNA genes, and microarray analysis.

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Additional info for Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications

Sample text

Thus the results from the optimization process are still interpretable. The system has been implemented and tested successfully in a sample application for the recognition of musical rhythm patterns. Hiroshi [66] proposes a new method for efficient finding of the biologically optimal alignment of multiple sequences. A key technique used in his method is deterministic annealing that attempts to find the global optimum in a parameter space through the annealing process. The author proposes a new simple probabilistic model for the usually time-consuming iterative process of deterministic annealing.

The interface between combinatorial optimization and fuzzy sets-based methodologies is the subject of a very active and increasing research. In this context, Balnco et al. [14] describe a fuzzy adaptive neighborhood search (FANS) optimization heuristic that uses a fuzzy valuation to qualify solutions and adapts its behavior as a function of the search state. FANS may also be regarded as a local search framework. The authors show an application of this fuzzy setsbased heuristic to the protein structure prediction problem in two aspects: (1) to analyze how the codification of the solutions affects the results and (2) to confirm that FANS is able to obtain as good results as a genetic algorithm.

There have been many successful projects in this area reported in the literature. For example, Fernando et al. [29] demonstrate how a supervised fuzzy pattern algorithm can be used to perform DNA microarray data reduction over real data. The benefits of their method can be employed to find biologically significant insights relating to meaningful genes in order to improve previous successful techniques. Experimental results on acute myeloid leukemia diagnosis show the effectiveness of the proposed approach.

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