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Biomedical research progresses rapidly, in particular in the area of genomic and postgenomic research. Hence many challenges appear for biostatistics and bioinformatics to deal with the large amount of data generated. After presenting some of these challenges, this chapter aims at presenting evolutionary combinatorial optimization approaches proposed to deal with knowledge discovery in bioinformatics. Therefore, the chapter will focus on three main tasks of data mining (association rules, feature selection, and clustering) widely encountered in bioinformatics applications. For each of them, a description of the task will be given as well as information about their uses in bioinformatics. Then, some evolutionary approaches proposed to cope with such a task will be exposed and discussed.

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Akaike information criterion


area under ROC curve

area under curve


Bayesian information criterion


bioinformatics-oriented hierarchical evolutionary learning


classification accuracy


correlation feature selection


deviance information criterion


deoxyribonucleic acid


evolutionary algorithm


evolutionary local selection algorithm


genetic algorithm


grammar-based genetic programming


genetic algorithm


genome-wide association studies


k nearest neighbor


learning classifier system


leave-one-out cross-validation


local search


minimum description length


multiple linear regression


multiobjective evolutionary algorithm




neural network


partial least square


root-mean-square error of prediction


receiver operating characteristic


roulette wheel selection


sequential backward selection


sequential forward selection


single nucleotide polymorphism


subset size-oriented common features


stochastic universal sampling


support vector machine


variance ratio criterion


Xie-Beni cluster validity index


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Hamon, J., Jacques, J., Jourdan, L., Dhaenens, C. (2015). Knowledge Discovery in Bioinformatics. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg.

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