A Methodology for Biologically Relevant Pattern Discovery from Gene Expression Data

Pensa, Ruggero G.; Besson, J.; Boulicaut, Jean-François

doi:10.1007/978-3-540-30214-8_18

Cited by 40 publications

(45 citation statements)

References 16 publications

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“…We need to measure both the degree of similarity of their structures and the similarity between the contents of their associated collections of clusters. We introduced in [16] a simple measure which is also easy to compute. Intuitively, it depends on the number of matching nodes between the two trees we have to compare.…”

Section: Comparing Binary Treesmentioning

confidence: 99%

“…In [16], we have proposed a method which supports the choice for a discretization technique and an informed decision about its parameters. The idea was to study the impact of discretization by a sound comparison between the dendrograms (i.e., binary trees) that are generated by the same hierarchical clustering algorithm applied to both the raw expression data and various derived boolean matrices.…”

Section: Introductionmentioning

confidence: 99%

“…The idea was to study the impact of discretization by a sound comparison between the dendrograms (i.e., binary trees) that are generated by the same hierarchical clustering algorithm applied to both the raw expression data and various derived boolean matrices. This paper is a significant extension of [16]. The framework has been revisited and the experimental validation have been considerably extended.…”

Section: Introductionmentioning

confidence: 99%

“…In Section 2, we refine the similarity measure introduced in [16]. It is level independent, and it depends for each node on its subtree structure.…”

Section: Introductionmentioning

confidence: 99%

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Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis

Pensa¹,

Boulicaut²

2005

Lecture Notes in Computer Science

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Abstract. In the domain of gene expression data analysis, several researchers have recently emphasized the promising application of local pattern (e.g., association rules, closed sets) discovery techniques from boolean matrices that encode gene properties. Detecting local patterns by means of complete constraint-based mining techniques turns to be an important complementary approach or invaluable counterpart to heuristic global model mining. To take the most from local set pattern mining approaches, a needed step concerns gene expression property encoding (e.g., over-expression). The impact of this preprocessing phase on both the quantity and the quality of the extracted patterns is crucial. In this paper, we study the impact of discretization techniques by a sound comparison between the dendrograms, i.e., trees that are generated by a hierarchical clustering algorithm on raw numerical expression data and its various derived boolean matrices. Thanks to a new similarity measure, we can select the boolean property encoding technique which preserves similarity structures holding in the raw data. The discussion relies on several experimental results for three gene expression data sets. We believe our framework is an interesting direction of work for the many application domains in which (a) local set patterns have been proved useful, and (b) Boolean properties have to be derived from raw numerical data.

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Section: Comparing Binary Treesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…In Section 2, we refine the similarity measure introduced in [16]. It is level independent, and it depends for each node on its subtree structure.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis

Pensa¹,

Boulicaut²

2005

Lecture Notes in Computer Science

Self Cite

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show abstract

“…In that context, each cell of the matrix is a quantitative measure of the activity of a given gene in a given biological sample. Several researchers have considered how to encode Boolean gene expression properties like, e.g., gene over-expression [1,7,12,11]. In such papers, the computed Boolean matrix has the same number of attributes than the raw data but it encodes only one specific property.…”

Section: Introductionmentioning

confidence: 99%

Mining Bi-sets in Numerical Data

Besson

Robardet

Raedt

et al. 2007

Lecture Notes in Computer Science

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Abstract. Thanks to an important research effort the last few years, inductive queries on set patterns and complete solvers which can evaluate them on large 0/1 data sets have been proved extremely useful. However, for many application domains, the raw data is numerical (matrices of real numbers whose dimensions denote objects and properties). Therefore, using efficient 0/1 mining techniques needs for tedious Boolean property encoding phases. This is, e.g., the case, when considering microarray data mining and its impact for knowledge discovery in molecular biology. We consider the possibility to mine directly numerical data to extract collections of relevant bi-sets, i.e., couples of associated sets of objects and attributes which satisfy some user-defined constraints. Not only we propose a new pattern domain but also we introduce a complete solver for computing the so-called numerical bi-sets. Preliminary experimental validation is given.

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Effect of data discretization on the classification accuracy in a high-dimensional framework

Tillander

2012

Int. J. Intell. Syst.

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We investigate discretization of continuous variables for classification problems in a high‐ dimensional framework. As the goal of classification is to correctly predict a class membership of an observation, we suggest a discretization method that optimizes the discretization procedure using the misclassification probability as a measure of the classification accuracy. Our method is compared to several other discretization methods as well as result for continuous data. To compare performance we consider three supervised classification methods, and to capture the effect of high dimensionality we investigate a number of feature variables for a fixed number of observations. Since discretization is a data transformation procedure, we also investigate how the dependence structure is affected by this. Our method performs well, and lower misclassification can be obtained in a high‐dimensional framework for both simulated and real data if the continuous feature variables are first discretized. The dependence structure is well maintained for some discretization methods. © 2012 Wiley Periodicals, Inc.

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A Methodology for Biologically Relevant Pattern Discovery from Gene Expression Data

Cited by 40 publications

References 16 publications

Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis

Boolean Property Encoding for Local Set Pattern Discovery: An Application to Gene Expression Data Analysis

Mining Bi-sets in Numerical Data

Effect of data discretization on the classification accuracy in a high-dimensional framework

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