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To study whether the predictions of the individual methods result in the same or complementary sets of predictions we determined the overlap of GO terms predicted by either strategy.

Figure 4b shows the experimental GO-term annotation of hPNPase as well as the terms predicted by a representative set of the ten top-performing methods.

The GO terms predicted by the system are compared with gold standard to calculate precision, recall and F-measure.

Yet, the quality of the GO terms predicted by machine learning continues to improve, thanks to the growing number of high-quality GO terms assignments available in GOA: since 2006, GOCat performances have improved by ∼50%.

In addition, we assessed the performance of: c) an indirect method that first predicts disease terms for a gene using a structured model and then maps them to HPO terms and d) using OMIM disease terms predicted by PhenoPPIOrth followed by mapping the OMIM terms to HPO terms.

Since a method that detects associations between a TF and highly specific GO terms is more useful than one that reports only general GO terms, we also measure the average depth of the most specific GO terms predicted by G omo.

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Sma3s can be easily adapted to use different annotation classes, and each term predicted by Sma3s can be assigned a corresponding probability value.

The average number of significant GO terms predicted for TFs by G omo increases accordingly (Table 1, 'GO terms per TF tested').

Terms predicted with high accuracy by the literature features do not show a similar tendency (data not shown).

In a multivariate model, knowledge of evolutionary terms was predicted by education and religious denomination.

We then consider PNFL and DREAM4 regulatory logics as approximately equal if the operator that combines the majority of terms is predicted by PNFL.

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