Table 1

Different regression-based methods applied to the time-series gene expression data to construct gene regulatory networks
Method Data used Description
iBMA-prior Gene expression + external data Our proposed methodology that incorporates prior model probabilities in BMA. These prior probabilities were computed using external data sources.
iBMA-shortlist Gene expression + external data Iterative BMA that uses external knowledge to shortlist p = 100 candidates for each target gene. The revised supervised step was used. Unlike iBMA-prior, the information from the external data is not used in variable selection via BMA.
Network A from Yeung et al. [3] Gene expression + external data This method is the same as in iBMA-shortlist, but using the old version of supervised step described in Yeung et al. [3]. We aim to study the impact of the revised supervised step by comparing iBMA-shortlist to network A.
LASSO-shortlist Gene expression + external data LASSO [36,63] with the use of external knowledge to shortlist p = 100 candidates for each target gene.
LAR-shortlist Gene expression + external data LAR [64] with the use of external knowledge to shortlist p = 100 candidates for each target gene.
iBMA-size Gene expression data only A simplified version of iBMA-prior that disregards external knowledge, except for setting πgr = τ = 2.76/6000 = 0.00046 for all g and r. This essentially turns Eq. (5) into a function of model size only.
iBMA-noprior Gene expression data only Iterative BMA without any use of external knowledge.
LASSO-noprior Gene expression data only LASSO without any use of external knowledge.
LAR-noprior Gene expression data only LAR without any use of external knowledge.

Lo et al.

Lo et al. BMC Systems Biology 2012 6:101   doi:10.1186/1752-0509-6-101

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