Sentence examples for mutagenicity prediction from inspiring English sources

The first bias was set for carcinogenicity and mutagenicity prediction that yielded only 5 compounds out of 42 entitled for the next screening step, which fundamentally is based on 'rule of five' (RO5) parameters.

Figure 12 Performance of the TopKat Ames mutagenicity prediction module on aryl-amines.

The pre-built mutagenicity prediction model available to us in TopKat [80] was explored as a possible prediction method.

The application of Gaussian processes, previously not applied to Ames mutagenicity prediction proves slightly superior to the other two methods.

The aim of the work presented here has been to develop an interpretation methodology for Ames mutagenicity prediction that is agnostic to the statistical machine learning algorithm used.

The excellent results achieved by this integrated model on the mutagenicity prediction issue open the way to a new era of hybrid models, customisable to meet different requirements.

The algorithm has provided meaningful interpretation of Ames mutagenicity predictions from both random forest and support vector machine models built on a variety of structural fingerprints.

After the prediction, mutagenicity of 2 anticancer peptides and 2 non-anticancer peptides was investigated by Ames test.

Her research activities include: development of algorithms for mutagenicity and carcinogenicity prediction, organization of chemical databases, validation of different schemas for toxicity data integration, mapping between different toxicological databases, and the development of ontology for toxicological endpoints.

In addition, toxic compounds were filtered out using a categorical SVM model for prediction of mutagenicity trained on Ames-test mutagenicity data (5-fold ROC score: 0.8948).

Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models.