Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine Learning
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
A proposal on reasoning methods in fuzzy rule-based classification systems
International Journal of Approximate Reasoning
Modeling genetic networks: comparison of static and dynamic models
Proceedings of the 2007 Summer Computer Simulation Conference
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The rapid development of methods that select over/under expressed genes from microarray experiments have not yet matched the need for tools that identify informational profiles that differentiate between experimental conditions such as time, treatment and phenotype. Uncertainty arises when methods devoted to identify significantly expressed genes are evaluated: do all microarray analysis methods yield similar results from the same input dataset? do different microarray datasets require distinct analysis methods?. We performed a detailed evaluation of several microarray analysis methods, finding that none of these methods alone identifies all observable differential profiles, nor subsumes the results obtained by the other methods. Consequently, we propose a procedure that, given certain user-defined preferences, generates an optimal suite of statistical methods. These solutions are optimal in the sense that they constitute partial ordered subsets of all possible method-associations bounded by both, the most specific and the most sensitive available solution.