The results of both generating new test sequences across land plants (Table 4) and in data mining GenBank (Table 3) demonstrate the utility of this approach.
For identifying LDL cholesterol ≥70 the PPV was 86% (95% CI: 83%, 89%) in the model using pre-specified variables, and increased to 91%9595% CI: 85%, 91%) when adding data mining variables (Table 2 and Additional file 1: Figure S2, Panel B).
In the model that included pre-specified variables, a predicted probability threshold of 0.55 yielded a PPV of 87%95%5% CI: 85%, 88%) for identifying high risk for CHD, and a sensitivity of 69%95%5% CI: 67%, 70%); results were similar after adding data mining variables (Table 2 and see Additional file 1: Figure S1, Panel A).
In the model using pre-specified variables, a predicted probability threshold of 0.28 yielded a PPV of 52%95%5% CI: 49%, 54%) for identifying very high risk for CHD events and a sensitivity of 63%95%5% CI: 59%, 66%); results were similar after adding data mining variables (Table 2 and see Additional file 1: Figure S1, Panel B).
Among the genes overexpressed in the rat pituitary adenomas, predicted to be up-regulated in at least 2 of the 3 human expression array studies by data mining (Supplementary Table 2, in bold), and never before associated with pituitary adenomas, are Cyp11a1 and Nusap1.
In the model using pre-specified variables, a predicted probability threshold of 0.20 yielded a PPV of 31%95%5% CI: 27%, 36%) for identifying Framingham CHD risk score >20% and a sensitivity of 47%95%5% CI: 43%, 54%); results were similar after adding data mining variables (Table 2 and see Additional file 1: Figure S1, Panel C).
Table 1 provides an overview of the variables that appear in the data mining datasets and Table 2 has the full names of the biomarkers of potential harm.
Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach.
The SEP database was then merged with mouse Uniprot database and Contamination database to form Mouse Merged database for mass spectrometry data mining in this study Table 1 Verification of the MMD Accession Sequence Pub.
Where there was a good correlation between probe set and intronic gene (e.g., 224741_x_at and 224841_x_at for RNU44), these microarray data can be mined for prognostic association, however, where there is a poor correlation (e.g., 227517_x_at or 228238_x_at), data mining was not possible (Table 1, Supplementary Figure 2).
