Research Centre for Cheminformatics
Our research is mostly private and confidential due to contracts with our clients. We can however say in which areas we performed research and development. In some cases, and if agreed by our client, we publish with them a scientific paper on lessons we together learnt during R&D.
Statistical modelling
We have participated in contract research projects related to regression, classificiation and survival modelling for over a decade. Our constant research questions are: How can we systematically improve the performance of predictive models we build? How best to automate it?
Non-applicability domain
Our starting premise is that all models and expert systems which predict something ought to generate "don't know" predictions. We think that it is more fruitful (and honest) to define model's non-applicability domain, rather than its applicability domain. Our research questions are: How best to define "don't know" predictions in practice? Why is it important?
Causal inference and Bayesian statistics
We have thoroughly studied Causal inference and Bayesian statistics but we have rarely had an opportunity to apply them in our contract research projects.
Stratified medicine
Since 2010 we have worked on contract research projects where, if possible, the goal was to find a sub-population which would have a better/worse survival than the remaining part of the population. Our research questions are: How to find such sub-population if it exists? What are the best statistics for measuring that?
Survival analysis
While working on numerous right-censored (survival) datasets, we have noticed how "fragile" they are, and how much our estimates depend a lot on certain data points, as well as on certain assumptions. Our research questions are: How best to create predictive models based on survival datasets? How best to report such models? What are the minimum requirements for a survival prediction model to be used in clinical practice?