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

Authors:
Chia-Chin Wu;Shahab Asgharzadeh;Timothy J. Triche;David Z. D'Argenio
Affiliations:
-;-;-;-
Venue:
Bioinformatics
Year:
2010

Citing 0
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Prediction of functional associations between proteins by means of a cost-sensitive artificial neural network

IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Synergistic effect of different levels of genomic data for cancer clinical outcome prediction

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An effective, practical and low computational cost framework for the integration of heterogeneous data to predict functional associations between proteins by means of Artificial Neural Networks

Neurocomputing

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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.