Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining structural databases: an evolutionary multi-objetive conceptual clustering methodology
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Filter versus wrapper gene selection approaches in DNA microarray domains
Artificial Intelligence in Medicine
Hi-index | 0.00 |
The rapid development of methods that select over/under expressed genes from RNA microarray experiments have not yet satisfied the need for tools that identify differential profiles that distinguish between experimental conditions such as time, treatment and phenotype. We evaluate several microarray analysis methods and study their performance, finding that none of the methods alone identifies all observable differential profiles, nor subsumes the results obtained by the other methods. Therefore, we propose a machine learning based methodology that identifies and combines the abilities of microarray analysis methods to recognize differential profiles. We encode the results of this methodology in decision making association rules able to decide which method or method-aggregation is optimal to retrieve a set of genes exhibiting a common profile. These solutions are optimal in the sense that they constitute partial ordered subsets of all method-aggregations bounded by the most specific and the most sensitive available solution. This methodology was successfully applied to a study of inflammation and host response to injury data set derived from the analysis of longitudinal blood microarray profiles of human volunteers treated with intravenous endotoxin compared to placebo. Our approach was able to uncover a cohesive set of differentially expressed genes and novel members exhibiting previously studied differential profiles. This guideline serves as a means to support decisions on new microarray problems.