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


BACKGROUND
dargenio@bmsr.usc.edu


BACKGROUND
Supplementary material is available at Bioinformatics online.
Department of Biomedical Engineering, University of Southern California, Los Angeles, 90089, USA.
Chia-Chin
CC
Wu
Shahab
S
Asgharzadeh
Timothy J
TJ
Triche
David Z
DZ
D'Argenio
D016208:Databases, Factual; D023281:Genomics / Q000379:methods*; D006801:Humans; D015398:Signal Transduction / Q000235:genetics*