Sentence examples for models performed poor from inspiring English sources

Similarly, Rfam-based covariance models performed poor to classify the novel/non-similar sequences whereas comparatively structural information based graph-properties of RNAcon method performed well because graph-properties based features provide both local as well as global structural features of a particular class.

These models perform poor with MCC 0.27, when evaluated on Pyrimidine class of molecules.

It is a well-known fact that machine learning techniques, particularly SVM-based models perform poor especially when positive and negative dataset is unbalanced.

However, the frequency-based model performed poor in this dataset as well [Additional file 7: Table S6].

Figure  2 shows an example of a short cyclic peptide (10 residues) where PEPstrMOD approached close to the native structure while the ab initio model performed poor.

For a couple of countries (Italy and the Netherlands), however, the autoregressive model performed poorer than the base non-linear model.

Again the models performed slightly poorer than the full, data-driven model in the ECRIC dataset, but somewhat better in the WMCIU validation dataset.

In almost all cases, these tests indicated that the Type B model performed best followed by the Type C model, with the Type A model invariably performing poorest (Table 8).

Despite the low frequency of poor outcomes both models performed well, with receiver operating characteristic curves of 0.75 for maternal outcomes and 0.78 for infant outcomes.

All three models performed comparably with slightly better performance by FIA's CRM method and slightly poorer performance using the Chojnacky estimates.

However, power performed poorer than theta band functional connectivity in terms of specificity and sensitivity in our regression models, indicating the added value of connectivity over power.