Sentence examples for by models built on from inspiring English sources

(iii) Verify in vitro affinity predicted by models built on SELEX data by electrophoretic mobility shift assay.

They succeeded by developing services that offered unfettered and easy access to something useful, often embracing some sort of sharing model for partners powered by revenue models built on network effects.

The fields are described by experimental-statistical models built on the data obtained in specially designed experiment at industrial conditions.

We also proposed a new method that automatically discovers angiogenesis-related tissue terms and trigger words, by comparing the language models built on the predicate argument relations of two ENJU-parsed corpora: one contained angiogenesis-specific documents and the other general biomedical texts.

The 'Functional Use' data is either based on reported data or predicted by functional use QSAR models built on the harmonized functional use categories derived from reported uses [63].

To further illustrate the potential prognostic application of pathway L values, we implemented classification models built on genes identified by a published methodology for gene expression analysis, and compared the resulting prediction performances against the results reported above.

It represents the variance in the flux data that is explained by a model built on the selected EMs.

The performance of the Set A model was actually better for Set B data than for the data used to train it while a model based on Set B had clear difficulty in predicting Set A. This can be quickly seen in Figure 10 which shows the ROC curve for Set B predicted by a PLS model built on Set A (solid red line, left graph) and the ROC curve for Set A predicted by Set B (solid orange line, right graph).

I'm encouraged by their business model built on social and ecological values, and hope they are a model for others.

Among genes motioned above, three genes have also been predicted by a target prediction model built on 12 transfection datasets with good prediction posterior probabilities and low FDR (<25%) [ 45].

The fragmentation patterns were characterized by using a forest fragmentation model built on the sliding window analysis technique in association with landscape indices.