STAHLSCHMIDT, SÖREN RICHARD2019-10-042019-10-042019-10-04http://hdl.handle.net/2077/62091The aim of this thesis is to predict different mechanisms of toxicity from the metabolomic response of HepG2 liver cells. In order to utilize the metabolomic data the a semisupervised machine learning approach is investigated, namely the cluster-then-label approach. The research focuses on the unsupervised part due to the centrality to this method. The dose-dependency within the data is modelled by clustering the dose-response curves according to their shape and transforming the feature space to a categorical one. This dataset is then clustered with the K-Modes algorithm. The analysis of the experimental data has shown that it is possible to distinguish toxic from non-toxic compounds on individual dose level though mechanisms can not clearly be distinguished. The proposed method is not able to clearly distinguish between toxic and non-toxic compounds or between the mechanisms of toxicity. It is hypothesized that the lack of mutually exclusive labels makes the prediction harder. Furthermore, the model could benefit from a more fine-grained dose levels in the identified range.engPredictive ToxicologyMetabolomicsSemi-Supervised LearningDoseResponsetext