Towards Ad-Hoc Rule Semantics for Gene Expression Data

Agier, Marie; Petit, Jean-Marc; Suzuki, Einoshin

doi:10.1007/11425274_51

Cited by 3 publications

(4 citation statements)

References 15 publications

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“…Applying symmetric distance measure, patterns of negatively (inversely) correlated genes can be also discovered by attribute profiles that are hidden by traditional patterns. Also note that our distance measure is close to the semantics of functional (multivalued) dependencies, see [1,13].…”

Section: By Discovering Attribute Profiles a Considerable Number Of mentioning

confidence: 85%

Frequent Pattern Discovery Without Binarization: Mining Attribute Profiles

Gyenesei

Schlapbach

Stolte

et al. 2006

Lecture Notes in Computer Science

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Frequent pattern discovery has become a popular solution to many scientific and industrial problems in a range of different datasets. Traditional algorithms, developed for binary (or Boolean) attributes, can be applied to such data with a prerequisite of transforming non-binary (continuous or categorical) attribute domains into binary ones. As a consequence of this binarization, the discovered patterns no longer reflect the associations between attributes but the relations between their binned independent values, and thus, interactions between the original attributes may be lost. In this paper we propose to overcome this limitation by introducing the concept of mining frequent attribute profiles that describes the relationships between the original attributes. By this concept, previously hidden interactions can be discovered and redundant patterns that are identified by traditional methods are eliminated. A novel algorithm, called MAP, has been developed for mining attribute profiles that can be potentially applied to diverse data domains. The effectiveness of the proposed method is shown by using gene expression or microarray data.

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Section: By Discovering Attribute Profiles a Considerable Number Of mentioning

confidence: 85%

Frequent Pattern Discovery Without Binarization: Mining Attribute Profiles

Gyenesei

Schlapbach

Stolte

et al. 2006

Lecture Notes in Computer Science

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“…This research topic is quite active currently and therefore, additional work can be found, as for example in [49][50][51] and others.…”

Section: Gene Expression Data Analysis Using Rule Miningmentioning

confidence: 99%

Discovering Meaningful Rules from Gene Expression Data

Dafas¹,

Garcez²

2007

CBIO

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Gene expression data are essential in understanding gene functions, biological networks, and cellular conditions. Over the last decade, with the use of DNA microarray technology, larger-scale gene expression data has become available and various data mining techniques have been used in an attempt to extract biologically relevant knowledge. Rule mining is a widely used approach in data mining and lately has attracted considerable interest in Bioinformatics with a number of methods being proposed over the last few years. Rule discovery can reveal biologically relevant associations between different genes or between experimental conditions and gene expression. The widely used data clustering techniques that can successfully identify co-expressed genes can be seen as a special case of rule mining. Furthermore, association rule mining can reveal temporal patterns of gene co-expression inferring participation in gene networks. This paper is a review of recent developments and current state of association rule mining methodologies in gene expression data. We discuss the advantages and limitations of the existing techniques and moreover, we propose a general framework under which association rule mining in gene expression can infer very often meaningful biological results.

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“…In [3], we proposed three new semantics for gene expression data and show their biological interests. In [2,4], we did a first step toward a formal framework with a first generic definition of a semantics and the notion of a well-formed semantics. In this paper, we go a step beyond by giving a new syntactical characterization of a wellformed semantics, leading to the theorem 1.…”

Section: Related Contributionsmentioning

confidence: 99%

“…In a data mining context, error and quality measures can be integrated a posteriori to sort and to qualify the rules. Furthermore, we focus on those semantics verifying Armstrong's axioms, so-called "well-formed semantics" [4], i.e. semantics for rules on which Armstrong's axiom system applies.…”

Section: Introductionmentioning

confidence: 99%