Ontology pre-training improves machine learning-based predictions for metabolites

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Recent advances in the field of machine learning have shown that integration of expert knowledge improves performances, in particular for complex domains such as biology. Bio-ontologies offer a rich source of curated biological knowledge that can be harnessed to this end. Here, we describe an intuitive and generalisable approach to embed the knowledge contained in a classification hierarchy derived from a bio-ontology into a machine learning model as an intermediate training step between general-purpose pre-training and task-specific fine-tuning in a process that we call ‘ontology pre-training’. We show that this approach leads to an improvement in predictive performance and a reduction in training time for a broad range of prediction tasks relevant to understanding metabolite functions in living systems, using a range of datasets derived from MoleculeNet. We see the biggest improvement for regression tasks, e.g. prediction of lipophilicity and aqueous solubility of molecules, and a robust improvement for most classification tasks. Our approach can be adapted for a wide range of knowledge sources, models and prediction tasks.

Authors 7

1. Charlotte Tumescheit

   University of Zurich

   Affiliation as printed

   University of Zurich;

2. Martin Glauer

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Otto-von-Guericke University Magdeburg;

3. Simon Flügel

   Osnabrück University

   Affiliation as printed

   Institute of Computer Science , Osnabrück University , Neuer Graben/ Schloss , 49074 Osnabrück , Germany,

4. Martin F. Larralde Aachen

   Leiden University Medical Center

   Affiliation as printed

   Leiden University Medical Center

5. Fabian Neuhaus

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Otto-von-Guericke University Magdeburg;

6. Till Mossakowski

   Osnabrück University

   Affiliation as printed

   Osnabrueck University;

7. Janna Hastings corresponding

   University of Zurich

   Affiliation as printed

   University of Zurich;