This paper proposes a new and very flexible data model, called gene expression graph (GEG), for genes expression analysis and classification. Three features differentiate GEGs from other available microarray data representation structures: (i) the memory occupation of a GEG is independent of the number of samples used to built it; (ii) a GEG more clearly expresses relationships among expressed and non expressed genes in both healthy and diseased tissues experiments; (iii) GEGs allow to easily implement very efficient classifiers. The paper also presents a simple classifier for sample-based classification to show the flexibility and user-friendliness of the proposed data structure.

A graph-based representation of Gene Expression profiles in DNA microarrays / Benso, Alfredo; DI CARLO, Stefano; Politano, GIANFRANCO MICHELE MARIA; Sterpone, Luca. - STAMPA. - (2008), pp. 75-82. (Intervento presentato al convegno IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) tenutosi a Sun Valley (ID), USA nel 15-17 Sept. 2008) [10.1109/CIBCB.2008.4675762].

A graph-based representation of Gene Expression profiles in DNA microarrays

BENSO, Alfredo;DI CARLO, STEFANO;POLITANO, GIANFRANCO MICHELE MARIA;STERPONE, Luca
2008

Abstract

This paper proposes a new and very flexible data model, called gene expression graph (GEG), for genes expression analysis and classification. Three features differentiate GEGs from other available microarray data representation structures: (i) the memory occupation of a GEG is independent of the number of samples used to built it; (ii) a GEG more clearly expresses relationships among expressed and non expressed genes in both healthy and diseased tissues experiments; (iii) GEGs allow to easily implement very efficient classifiers. The paper also presents a simple classifier for sample-based classification to show the flexibility and user-friendliness of the proposed data structure.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/1844482
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