The genome-wide hierarchical classification of gene functions, using biomolecular data from high-throughput biotechnologies, is one of the central topics in bioinformatics and functional genomics. In this paper we present a multilabel hierarchical algorithm inspired by the “true path rule” that governs both the Gene Ontology and the Functional Catalogue (FunCat). In particular we propose an enhanced version of the True Path Rule (TPR) algorithm, by which we can control the flow of information between the classifiers of the hierarchical ensemble, thus allowing to tune the precision/recall characteristics of the overall hierarchical classification system. Results with the model organism S. cerevisiae show that the proposed method significantly improves on the basic version of the TPR algorithm, as well as on the Hierarchical Top-down and Flat ensembles.

Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction / G. Valentini, M. Re. ((Intervento presentato al 1. convegno European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases : 1st International Workshop on learning from Multi-Label Data tenutosi a Bled, Slovenia nel 2009.

Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction

G. Valentini
Primo
;
M. Re
Ultimo
2009

Abstract

The genome-wide hierarchical classification of gene functions, using biomolecular data from high-throughput biotechnologies, is one of the central topics in bioinformatics and functional genomics. In this paper we present a multilabel hierarchical algorithm inspired by the “true path rule” that governs both the Gene Ontology and the Functional Catalogue (FunCat). In particular we propose an enhanced version of the True Path Rule (TPR) algorithm, by which we can control the flow of information between the classifiers of the hierarchical ensemble, thus allowing to tune the precision/recall characteristics of the overall hierarchical classification system. Results with the model organism S. cerevisiae show that the proposed method significantly improves on the basic version of the TPR algorithm, as well as on the Hierarchical Top-down and Flat ensembles.
Settore INF/01 - Informatica
http://homes.dsi.unimi.it/~valenti/papers/vale-re-ECML09.revised.pdf
Weighted True Path Rule: a multilabel hierarchical algorithm for gene function prediction / G. Valentini, M. Re. ((Intervento presentato al 1. convegno European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases : 1st International Workshop on learning from Multi-Label Data tenutosi a Bled, Slovenia nel 2009.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2434/154708
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