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Clustering optimal de gènes fondé sur une mesure de similarité sémantique

Identifieur interne : 001271 ( Main/Merge ); précédent : 001270; suivant : 001272

Clustering optimal de gènes fondé sur une mesure de similarité sémantique

Auteurs : Rachid Hafiane [France] ; Malika Smaïl-Tabbone [France] ; Marie-Dominique Devignes [France] ; Salvatore Tabbone [France]

Source :

RBID : Hal:hal-00920700

English descriptors

Abstract

In various application domains of knowledge extraction or information retrieval, objects are not represented as feature vectors in a vector space but as a pairwise similarity matrix. In molecular biology, such a similarity measure either captures the object structure (e.g. molecules, proteins as sequences of amino acids) or the semantics of their description (genes or diseases described with ontology terms). The numerous existing similarity measures often violate metricity properties. This is the case of our IntelliGO semantic similarity defined as a generalized cosine between two vectors of Gene Ontology terms (Gene Ontology is a directed acyclic graph representing the semantic relationship between terms). Specific techniques exist for embedding pairwise data into Euclidian space for facilitating subsequent clustering of the objects. We report in this paper comparative gene clustering with and without embedding using the Intelligo measure and benchmarks. As for the clustering algorithm, we use an implementation of the C-means algorithm taking as input either a distance" matrix or a set of vectors. We evaluate the clustering quality and discuss the results.

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Hal:hal-00920700

Le document en format XML

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<term>Embedding</term>
<term>Fuzzy C-means</term>
<term>Hierarchical ascending clustering</term>
<term>IntelliGO similarity measure</term>
<term>non metric dissimilarity</term>
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<div type="abstract" xml:lang="en">In various application domains of knowledge extraction or information retrieval, objects are not represented as feature vectors in a vector space but as a pairwise similarity matrix. In molecular biology, such a similarity measure either captures the object structure (e.g. molecules, proteins as sequences of amino acids) or the semantics of their description (genes or diseases described with ontology terms). The numerous existing similarity measures often violate metricity properties. This is the case of our IntelliGO semantic similarity defined as a generalized cosine between two vectors of Gene Ontology terms (Gene Ontology is a directed acyclic graph representing the semantic relationship between terms). Specific techniques exist for embedding pairwise data into Euclidian space for facilitating subsequent clustering of the objects. We report in this paper comparative gene clustering with and without embedding using the Intelligo measure and benchmarks. As for the clustering algorithm, we use an implementation of the C-means algorithm taking as input either a distance" matrix or a set of vectors. We evaluate the clustering quality and discuss the results.</div>
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