We also want to know, when GBA does work, is it because of "generic features" such as node degree, or are the GBA methods working the way most computational biologists hope they are working, which is inferring specific things about a gene based on specific features of its network neighbors.
Most computational approaches rely on the integration of several sources of heterogeneous data such as sequence features, gene expression data and protein protein interactions (PPIs).
In reality, most computational models of most things have, historically speaking, been wrong — or at least incomplete, effective in some circumstances, not all.
We show the computational complexity7 in relative terms with respect to the most computational demanding solution.
We have also demonstrated that computational methods can extract the most prominent features of the transcriptional regulation captured by genomic data.
Number of required element in the SEM to achieve acceptable results is much fewer than other numerical methods like the FEM and the consequent saving in computational cost is one of the most interesting features of the SEM.
The most effective features should have both significant biological and computational importance.
It is important to design statistical and computational techniques capable of handling the available data and highlighting the most important features.
It would be helpful to explain in the next sentence what those most relevant features are... Other than that, this is certainly a strong computational study worth publishing and hopefully yeast biologists will make use of information presented here.
Arguably the most important feature of a computational model is that it generates novel hypotheses that can be tested experimentally.
