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.

Krouk et al. Genome Biology 2010 11:R123   doi:10.1186/gb-2010-11-12-r123