Therefore, the predictions with large positive regressed values can be used as positive examples for training other local models, and those with large negative regressed values can be used as negative examples, where the absolute regressed values represent the confidence.
Using predictions with a positive score and a significant folding p-value we identified from our sequences 47 (22 with expression evidence for star sequence) new miRNAs in chimpanzee, 240 (166 with expression evidence for star sequence) in gorilla, 55 (13 with expression evidence for star sequence) in orangutan and 47 (24 with expression evidence for star sequence) in rhesus macaque.
Further we show that putative heterozygous SNP positions that are present at the expected 50∶50 allelic ratio are the most robust predictions with lowest false positive rate.
The accuracy is defined as where TP is the number of true positives (binding residues with positive predictions); TN is the number of true negatives (non-binding residues with negative predictions); FP is the number of false positives (non-binding residues but predicted as binding sites) and FN is the number of false negatives (binding residues but predicted as non-binding sites).
The accuracy is defined as where TP is the number of true positives (binding residues with positive predictions); TN is the number of true negatives (non-binding residues with negative predictions); FP is the number of false positives (non-binding residues but predicted as binding residues) and FN is the number of false negatives (binding residues but predicted as non-binding residues).
Other variables included using the classification and regression trees methodology, besides those identified by the logistic regression were chest pain, with positive prediction, and dyspnea, with negative prediction ability.
However, each approach produce widely different lists of predictions with significant false-positive and false-negative rates [ 48].
Compared with the other genome-aware methods above, it was shown to be far more reliable with respect to precision (i.e. high-quality predictions with few false positives) [ 10], albeit with lower coverage as expected.
In practice, this is a useful feature since follow-up studies tend to be time consuming and expensive and, hence, it is important for the experimentalist to have causal predictions with low false-positive rates.
Within these brain regions of interest, positive prediction error time courses were constructed from the average of the signals from all trials with positive prediction errors.
