Journal Article

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

Chia-Chin Wu, Shahab Asgharzadeh, Timothy J. Triche and David Z. D'Argenio

in Bioinformatics

Volume 26, issue 6, pages 807-813
Published in print March 2010 | ISSN: 1367-4803
Published online February 2010 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btq044
Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

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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.

Journal Article.  6112 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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