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A companion paper describes learning curves for four of these 12 data sets, which describe an alternative mutual-information prediction metric and NPAIRS reproducibility as a function of training-set sizes from 2 to 18 subjects.

The mutual information between the predictions of methods A and B was defined as MI(A, B) = H(A)+H(B -H A, B -H Aere H(A) = -Σp(a)·log2p(a), H(A, B) = -ΣΣp(a, b)·log2p(a, b) and p(a) and p(b) are the marginal probability distributions of the predictions of methods A and B (i.e. the fraction of positive and negative CTFPs identified by each method, respectively).

We found that the alternative outcome-guided mutual information network further improved the prediction power of the network-based Cox regression.

Besides, we can consider use of other measures representing the correlation of a residue with a base instead of mutual information to further improve our prediction method.

In this sense, the mutual information indicates how much the prediction error of the state of v i changes if we know the state of v k.

Each method uses a different detection principle: SH applies χ2 or B statistics [ 32, 39]; BEAM uses Bayesian inference or B statistics; FIM, LRIT and LR are based on the logistic regression model; IG ranks SNPs by mutual information; MDR selects SNPs via prediction error; MECPM uses BIC to rank interactions and to assess statistical significance.

Our results show that the mutual information based network can further improve prediction performance in survival analyses.

To illustrate the general topology of the mutual information network and its effect on prediction performances more closely, we constructed gene interaction sub-networks by using the 100 edges with the largest mutual information values for each profile.

This result hints that indeed the mutual information can pick up the relationship between the prediction of the model and the real data that the GCF ignores.

The predictions of the four methods are not significantly correlated to one another in terms of mutual information, although their overlap in terms of their positive predictions is low yet significant.

Self-supervised models of how the brain represents and categorizes the causes of its sensory input can be divided into those that minimize the mutual information among evoked responses and those that minimize the prediction error.