Sentence examples for candidate regulation from inspiring English sources

Another strategy is, given a target gene g ∈ G, to jointly estimate the scores s(t g) for all candidate regulators t ∈ T g simultaneously, with a method able to capture the fact that a large score for a candidate regulation (t g) is not needed if the apparent correlation between t and g is already explained by other, more likely regulations.

In order to infer the regulatory network from the expression data X, we compute a score s : E → R to assess the evidence that each candidate regulation is true, and then predict as true regulation the pairs (t, g ) ∈ E for which the evidence s t, g) is larger than a threshold δ.

In summary, the full procedure for scoring all candidate edges in E, which we call TIGRESS, splits the GRN inference problem into p independent regression problems taking each target gene g ∈ G in turn, and scores each candidate regulation (t g) for a candidate TF t ∈ T g with the original (3) or area (4) stability score applied to LARS feature selection.

Once a score s g (t) is chosen to assess the significance of each transcription factor in the target-gene-specific regression model (1), we can combine them across all target genes by defining the score of a candidate regulation (t, g ) ∈ E as s(t g)= s g (t), and rank all candidate regulations by decreasing score for GRN inference.

The direct use of LARS to score candidate regulations has, however, two shortcomings.

Since we want to aggregate the predicted regulations across all target genes to obtain a global ranking of all candidate regulations, we need such a score.

For example, the correlation or mutual information between the expression levels of t and g along the different experiments is a popular way to score candidate regulations [ 12- 14].

To overcome both issues, we do not directly score candidate regulations with the LARS, but instead perform a procedure known as stability selection[ 26] on top of LARS.

Among the total i × p candidate regulations, the regulation with the maximum absolute value of R1 X, Y, i, p) is selected as the regulatory relation between genes X and Y.

The set of all candidate regulations is therefore E = (t, g ), g ∈ G, t ∈ T g, and the GRN inference problem is to identify a subset of true regulations among E. For that purpose, we assume we have gene expression measurements for all genes G in n experimental conditions.

In other words, we only focus on finding a good ranking of the candidate regulations E, by decreasing score, such that true regulations tend to be at the top of the list; we let the user control the level of false positive and false negative predictions he can accept.