What prompted the idea for the tool?
Pauling & Rose: The idea came up while we were involved in a study that required us to understand the heterogeneity of the patient cohort. We focused on a group of algorithms that could technically accomplish this, but we did not know which algorithm we should use and how we had to fine-tune the respective initial parameters. That is why we developed the MoSBi approach, which can automatically combine the results of multiple algorithms. The visualization aspect was needed because we required an intuitive view of the results to make them easier to interpret.
What exactly did you examine in the study pertaining to the MoSBi approach? What was your conclusion?
Pauling & Rose: The MoSBi publication focuses on bioinformatics. Bioinformatics method development requires thorough evaluation. We subsequently used public, molecular data from published studies and simulated data to address certain data characteristics. It allowed us to assess the strengths and weaknesses of MoSBi compared to other methods and to develop application scenarios. Of course, the method is also described in detail and defined mathematically in the study.
In another publication on nonalcoholic fatty liver disease, we used MoSBi to identify subsets based on lipid signatures for disease progression. We were able to show that the method can also be used in practical settings to deliver important insights.
What is the potential of algorithms or machine learning in medicine in the future?
Pauling & Rose: Machine learning can facilitate more accurate diagnoses or treatment options by combining and analyzing vast amounts of data. One can also systematically include the patient’s medical history, for example. It is primarily intended to be a clinical decision support tool for doctors. The legal framework outlining the digitization of patient records is obviously a key aspect in all this, enabling standardization in their use and contribution to research, if patients are on board.