To measure relative brain mass for the species in the tree we performed a phylogenetically controlled GLS regression analysis between log(brain mass) and log(body mass) using ML in BayesTraits (see Methods), that returned the following fit line: log(brain mass) = 2.18 + 0.684 [log(body mass)].
As a baseline method to evaluate against, we use the naïve method that returns the molecular formula with the smallest mass difference to the measured parent mass.
The clustering method that returns a higher value of Prenrich is used to obtain the mRNA clusters that are provided as an input to Step 2 of our method.
Lattice sheet microscopy coupled with structured illumination microscopy, a method that returns 3D images with resolution ~ 200 nm x 200 nm in the x/z plane that exceeds the diffraction limit, was applied to study the ultrastructural organization of germ granules in C. elegans [ 39].
In order to determine whether the user wants to play again, we need to write a playAgain method that returns a boolean telling the game whether the user has determined to play again or not.
We need to write a compareMoves method that returns 0 if the moves are the same, 1 if the current move beats the other move, and -1 if the current move loses to the other move.
Unlike GSEA or other enrichment methods that usually return p-values for gene set enrichment, DIRAC builds a network-based molecular signature that identifies robust differences in pathway activity between two disease states.
For estimation methods that do not return an F' as standard output (e.g., mlne), we multiplied estimates by the ratio C/G to calculate corrected Nb or Nbf (e.g., Turner et al. 2006).
For the sake of uniformity, we introduce a database size dependent effective variable x db, which is simply the E-value for search methods reporting E-values and is a monotonically decreasing function of quality score, e-(Quality score), for search methods that do not return E-values; see Table 2. Scaling of the database size and functional transformation to yield calibrated E-values.
AUC is a measure of choice when assessing the performance of methods that returns continuous scores such as the method we use in the prediction step.
