The submitted algorithms were then assessed for correctness, relying primarily on a novel gene-centric metric that allowed partial credit for predicting a "similar" term, based on proximity in the GO term graph.
Detailed program analysis was conducted on simulated data where alignment correctness could be assessed.
For each recorded diagnosis in the CCR, the level of correctness was assessed as: total correctness when the recorded diagnosis in the CCR corresponded well to the information in the VCR, partial correctness when the level of correctness was less satisfactory, or absence of correctness for incorrect information present in the CCR.
Correctness can in fact only be assessed afterwards.
The correctness of the POD process is assessed by, for example, checking convergence and stability of the iterative estimation process (i.e. formal errors of, and correlations between, estimated parameters), the observation fit and overlap analysis between consecutive orbit solutions.
Correctness of diagnostic information was assessed using semi-subjective criteria (table 1) under the categories "total correctness", "partial correctness", and "absence of correctness".
Further, its conformational correctness and stability enhancement was assessed using molecular dynamics (MD) simulations.
Correctness of the answers was assessed in relation to the available literature and EMHG guidelines on the subject.
Model completeness was assessed by comparing the number of model elements and input elements, while for correctness the properties of the model elements and input elements were compared.
The correctness, robustness, efficiency and relevance [ 7] of the statistical methods reported in the sample papers were assessed using a yes or no assignment for each characteristic.
