Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning
Handbook of Evolutionary Computation
Handbook of Evolutionary Computation
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Selecting Models from Data: AI and Statistics IV
Selecting Models from Data: AI and Statistics IV
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Modeling genetic networks: comparison of static and dynamic models
Proceedings of the 2007 Summer Computer Simulation Conference
Decision making association rules for recognition of differential gene expression profiles
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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The increased availability of biological databases containing representations of complex objects permits access to vast amounts of data. In spite of the recent renewed interest in knowledge-discovery techniques (or data mining), there is a dearth of data analysis methods intended to facilitate understanding of the represented objects and related systems by their most representative features and those relationship derived from these features (i.e., structural data). In this paper we propose a conceptual clustering methodology termed EMO-CC for Evolutionary Multi-Objective Conceptual Clustering that uses multi-objective and multi-modal optimization techniques based on Evolutionary Algorithms that uncover representative substructures from structural databases. Besides, EMO-CC provides annotations of the uncovered substructures, and based on them, applies an unsupervised classification approach to retrieve new members of previously discovered substructures. We apply EMO-CC to the Gene Ontology database to recover interesting substructures that describes problems from different points of view and use them to explain inmuno-inflammatory responses measured in terms of gene expression profiles derived from the analysis of longitudinal blood expression profiles of human volunteers treated with intravenous endotoxin compared to placebo.