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Bioinformatics. 2010 Mar 15;26(6):807-13. doi: 10.1093/bioinformatics/btq044. Epub 2010 Feb 4.

Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning.

Author information

1
Department of Biomedical Engineering, University of Southern California, Los Angeles, 90089, USA.

Abstract

MOTIVATION:

Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set, large-scale learning and massive missing data values.

RESULTS:

The proposed graph-based approach generates a robust GSN for the training process of genetic network construction. The RVM-based ensemble model that combines AdaBoost and reduced-feature yields improved performance on large-scale learning problems with massive missing values in comparison to Naïve Bayes.

CONTACT:

dargenio@bmsr.usc.edu

SUPPLEMENTARY INFORMATION:

Supplementary material is available at Bioinformatics online.

PMID:
20134029
PMCID:
PMC2832827
DOI:
10.1093/bioinformatics/btq044
[Indexed for MEDLINE]
Free PMC Article

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