Towards ad-hoc rule semantics for gene expression data
被引:0
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作者:
Agier, M
论文数: 0引用数: 0
h-index: 0
机构:
DIAGNOGENE, F-15000 Aurillac, FranceDIAGNOGENE, F-15000 Aurillac, France
Agier, M
[1
]
Petit, JM
论文数: 0引用数: 0
h-index: 0
机构:DIAGNOGENE, F-15000 Aurillac, France
Petit, JM
Suzuki, E
论文数: 0引用数: 0
h-index: 0
机构:DIAGNOGENE, F-15000 Aurillac, France
Suzuki, E
机构:
[1] DIAGNOGENE, F-15000 Aurillac, France
[2] Univ Clermont Ferrand, CNRS, LIMOS, UMR 6158, F-63177 Clermont Ferrand, France
[3] Yokohama Natl Univ, Yokohama, Kanagawa 2408501, Japan
来源:
FOUNDATIONS OF INTELLIGENT SYSTEMS, PROCEEDINGS
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2005年
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3488卷
关键词:
D O I:
暂无
中图分类号:
TP18 [人工智能理论];
学科分类号:
081104 ;
0812 ;
0835 ;
1405 ;
摘要:
The notion of rules is very popular and appears in different flavors, for example as association rules in data mining or as functional (or multivalued) dependencies in databases. Their syntax is the same but their semantics widely differs. In this article, we focus on semantics for which Armstrong's axioms are sound and complete. In this setting, we propose a unifying framework in which any "well-formed" semantics for rules may be integrated. We do not focus on the underlying data mining problems posed by the discovery of rules, rather we prefer to emphasize the expressiveness of our contribution in a particular domain of application: the understanding of gene regulatory networks from gene expression data. The key idea is that biologists have the opportunity to choose-among some predefined semantics-or to define the meaning of their rules which best fits into their requirements. Our proposition has been implemented and integrated into an existing open-source system named MeV of the TIGR environment devoted to microarray data interpretation.