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Table 2 The quality of fit of our state-space model approach slightly outperforms the non-SSM approaches

From: Predictive network modeling of the high-resolution dynamic plant transcriptome in response to nitrate

    Best hyper parameters (with respect to SNR on leave-1 training dataset) Performed on training set: Performed on test set:  
Dynamics Normalization Optimization Gamma (state-space coefficient) Tau (kinetic time constant) Lambda (regularization parameter) SNR (in dB) on leave-1 training dataset percentage of correct signs on leave-1 test dataset Reference
Kinetic MAS5 Gradient 1 3 0.0001 32.4 68% This work
Kinetic MAS5 LARS 0.1 3 0.1 32.4 74% This work
Kinetic MAS5 LARS 0 3 0.05 32.1 74% [33]
kinetic MAS5 Elastic Nets 0 3 0.05 32.1 74% [35]
Brownian MAS5 Gradient 0 NA 0.005 32.1 66% [34]
Brownian MAS5 LARS 0 NA 0.05 32.1 63% [34]
Brownian MAS5 Elastic Nets 0 NA 0.05 32.1 63% [34]
Naïve trend prediction MAS5   NA NA NA NA 52%  
  1. We compared our SSM-based technique (with a non-zero SSM parameter gamma) to previously published algorithms for learning gene regulation networks by enforcing gamma = 0 (see Materials and methods). We notice that the LARS algorithm [42], used in the Inferelator by Bonneau et al. [32, 33], as well as Elastic Nets [35, 43], obtain a slightly worse quality of fit (signal-to-noise ratio (SNR), in dB) than when combined with our state-space modeling for the same leave-out-last (leave-1) performance as our SSM plus LARS. Not using an mRNA degradation term, as in Wang et al. [34], degrades the leave-out-last performance.