As aforementioned, dcGO measured relevance between domain and GO term by P value while other methods calculated it based on conditional probability.
However, students reported significant gains on the retrospective postunit item measuring relevance, evaluating their perceptions of the connection between biology content and their personal lives ("before this unit" mean = 3.88; "now" mean = 5.64; Figure 4).
Based on these analyses, we proposed a novel strategy, namely RSC, to measure the relevance between domain and ancestral term based on semantic coverage.
In (5), the function fr(f ij ) measures the relevance between feature f ij (included in X i ) and target value via a feature-ranking strategy.
As (1), symmetrical conditional probability may be appropriate to measure the relevance between GO term go i and domain d j, DR(go i, d j ).
Therefore, p k * i) (l+1≤ i≤ n) measures the relevance between the k-th class and an unlabeled image bag x i.
Motivated by Brin and Page (1998); Tong et al. (2006), we consider to further develop standard random walk and use its equilibrium probability to measure the relevance between a group set and an unlabeled data point.
Since all data points (gene expression image bags), stage terms and annotation terms are equally regarded as vertices on, our task is to measure the relevance between a class/anntotation term vertex and a data point vertex.
The close solution to Equation (3) is (4) m ˜ = 1 − α I − α M ― − 1 m Similarly, graph Laplacian scores can be derived to measure the relevance between the phenotypes and the target phenotype p with the close solution (5) p ˜ = 1 − β I − β P − − 1 p where P ¯ is the normalized P and parameter β ∈ (0, 1).
Generally speaking, it can be concluded that RSCP is a more suitable measure of relevance between domain and GO term.
