While there are many computational techniques proposed for this network reconstruction task, their accuracy is consistently limited by the small number of high-confidence examples, and the uneven distribution of these examples across the potential interaction space, with some objects having many known interactions and others few.
To produce a simulated population data set for any one of the five examples, a set of 10,000 auxiliary variable values were generated, based on the frequency distribution of that example.
In the initial distribution of this example, there are no successfully designed RNA sequences, ie, no solution has a similarity score of 1.0.
Splitting the documents into 10 subsets required for 10-fold cross validation would have created an uneven distribution of the annotated examples and, while some of the sets would have too many annotated gold standard examples, some sets would have included too few.
c The average energy distribution of the two examples in frequency direction.
We first calculate the average energy distribution of the two examples in the frequency direction, which is shown in (c).
The two SVM models partition the space around both hyperplanes into bins, and the distribution of the training examples in individual bins is computed.
Large changes in the polar character (charge distribution) of the solvent, for example, may have an effect on the course of the reaction if there is a substantial change in polarity between the reactants and the transition state.
The percentage of participants over the range of the occupational noise index also conveys the skewedness in the distribution of the index, for example, less than 25% of the participants have noise index values in the upper half of the range.
Because machine learning methods rely on the assumption that the distribution of test examples is the same as the training examples, this bias makes the NA and NP tasks more difficult than performing well in CV.
