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Ontology-driven Co-clustering of Gene Expression Data

Contributo in Atti di convegno
Data di Pubblicazione:
2009
Abstract:
The huge volume of gene expression data produced by microarrays and other high-throughput techniques has encouraged the development of new computational techniques to evaluate the data and to formulate new biological hypotheses. To this purpose, co-clustering techniques are widely used: these identify groups of genes that show similar activity patterns under a specific subset of the experimental conditions by measuring the similarity in expression within these groups. However, in many applications, distance metrics based only on expression levels fail in capturing biologically meaningful clusters. We propose a methodology in which a standard expression-based co-clustering algorithm is enhanced by sets of constraints which take into account the similarity/dissimilarity (inferred by the Gene Ontology, GO) between pairs of genes. Our approach minimizes the intervention of the analyst within the co-clustering process. It provides meaningful co-clusters whose discovery and interpretation is increased by embedding GO annotations.
Tipologia CRIS:
04A-Conference paper in volume
Elenco autori:
F. Cordero; R. G. Pensa; A. Visconti; D. Ienco; M. Botta
Autori di Ateneo:
BOTTA Marco
CORDERO Francesca
PENSA Ruggero Gaetano
VISCONTI Alessia
Link alla scheda completa:
https://iris.unito.it/handle/2318/67372
Link al Full Text:
https://iris.unito.it/retrieve/handle/2318/67372/8138/aixia_4aperto_425186.pdf
Titolo del libro:
11th International Conference of the Italian Association for Artificial Intelligence: Emergent Perspectives in Artificial Intelligence, AI IA 2009
Pubblicato in:
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Journal
LECTURE NOTES IN ARTIFICIAL INTELLIGENCE
Series
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