BioLizard provides the best of both worlds: biological knowledge + computational expertise

Last week our Director of business development, Liesbeth Ceelen, presented BioLizard at the AI4Diag event, AI4 The health value chain, hosted by flanders.health.  She gave a brief overview of the range of expertise we have at BioLizard, specifically our proven experience in machine learning and artificial intelligence.

Liesbeth emphasized that, “For us at BioLizard, it is crucial to bring together both biological data and AI.”  Two of the major applications where we apply machine learning and AI tools are firstly for multi-omics data analysis/multimodality testing where we incorporate molecular data, clinical data as well as imaging data, and secondly for algorithm development.

During the presentation, Liesbeth provided some high-level examples where BioLizard has implemented machine learning and AI for specific client cases. One case focused on building predictive models for actual experimental data, while the other explored existing datasets using Natural Language Processing in order to generate a combination of models that would suggest optimally fitted sequences for clinical use.

Our workflow was more of an iterative process where we implemented various data transformations techniques and tested them using our panel of models to assess the predictive power for each dataset. Furthermore, we took into account and took steps to improve the completeness of the dataset, the highly volatile nature of the data, dataset size, as well as the fact that the data may be highly dimensional. All this together motivated the implementation and assessment of the various data transformation techniques to deal with this specific dataset.

All in all, our results provided our clients with robust models to be used for further studies and indicated the type of data needed for improved predictive capacity. We also highlighted important caveats to avoid in future experimental designs.

Concluding her remarks, Liesbeth stated that, “Machine learning based models are very useful to learn more about what scientific information is hidden in your data. However, machine learning is not magic and a proper data set size, quality of data and experimental design are crucial when developing predictive models.”