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

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

			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%

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.

ISSN: 1474-760X