Data integration and the unbalance between functionally annotated and unannotated genes are relevant items in the context of network-based gene function prediction. Even if both these topics have been analyzed in recent works, to our knowledge no network integration methods, specific for unbalanced functional classes have been proposed in this context. We introduce an unbalance-aware network integration method based on the recently proposed COSNet algorithm, and we apply it to the genome-wide prediction of Gene Ontology terms with the M. musculus model organism.

An unbalance-aware network integration method for gene function prediction / M. Frasca, A. Bertoni, G. Valentini. ((Intervento presentato al convegno MLSB 2013 - Machine Learning for Systems Biology tenutosi a Berlin nel 2013.

An unbalance-aware network integration method for gene function prediction

M. Frasca
Primo
;
A. Bertoni
Secondo
;
G. Valentini
Ultimo
2013-07

Abstract

Data integration and the unbalance between functionally annotated and unannotated genes are relevant items in the context of network-based gene function prediction. Even if both these topics have been analyzed in recent works, to our knowledge no network integration methods, specific for unbalanced functional classes have been proposed in this context. We introduce an unbalance-aware network integration method based on the recently proposed COSNet algorithm, and we apply it to the genome-wide prediction of Gene Ontology terms with the M. musculus model organism.
Settore INF/01 - Informatica
ISCB
An unbalance-aware network integration method for gene function prediction / M. Frasca, A. Bertoni, G. Valentini. ((Intervento presentato al convegno MLSB 2013 - Machine Learning for Systems Biology tenutosi a Berlin nel 2013.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/224158
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