Analyses in this study were performed in a 'data-driven' way, by means of computational statistics without prior implementation of existing knowledge about functionally related genes and pathways.
It is noted that the way of the computational burden reduction in this article is different from [12, 13]: herein, we focus on reducing the number of running optimal filtering procedures, while [12, 13] focus on improving the computational efficiency of the optimal filtering procedure.
By way of contrast, computational approaches that take advantage of the rapid accumulation of massive data may provide a more promising and desirable method for multicomponent drug studies.
One may further look at various sparseness conditions as a way of improving computational efficiency and incorporating pre-existing knowledge about the network during inference.
Variance reduction methods, which aim to decrease the Monte Carlo error, are another useful way of reducing computational time: because the Monte Carlo error is lower, less simulations need to be run for a given accuracy, saving time.
By way of comparison, computational searches of 39 orthologous human introns (58,313 bp) identified 6 candidate miRNAs, 1 CD type snoRNA, and no candidate HACA type snoRNAs (Additional files 4 and 5); none of these human introns contain annotated miRNAs or snoRNAs within the current human genome assembly [ 22].
Although for a fixed system of basis functions the matrix exponentiation (10) provides the exact solution avoiding time discretisation at all, the condition (14) motivates to consider the CME on sequential time steps, not as means of time approximation, but as a way of economy of computational resources.
The hope is also that the simulation leads to innovative computer processing systems and insights into the way that lots of computational elements can be hooked up to each other.
In this way, the results of computational simulations can be better understood and interpreted, regardless of the model complexity.
