Faculty of Computer Science

Research Group Theoretical Computer Science

Oberseminar: Heterogene formale Methoden

Abstract:

Machine learning, having been used in a myriad of fields successfully for many years, has also been used in the cheminformatics field for tasks such as predicting chemical and biological properties from molecular structure. (Mossakowski, Neuhaus, Glauer, Memariani, Hastings, 2021) experimented with applying machine learning to the task of structure-based chemical ontology classification and found out that machine learning provided promising results for this particular task. Machine learning here is important due to the fact that chemical datasets are too large for manual classification into an ontology. Ensemble classifiers are a combination of different machine learning classifiers which offer an improved classification performance as compared to a single classifier. In this thesis, we will experiment with ensemble classifiers for structure-based chemical ontology classification to see whether we could further improve the performance previously obtained.

Keywords: Structure-based chemical ontology classification, chemical ontology, ChEBI, machine learning, ensemble classifier, output combination, automated classification, LSTM