Multimodal learning is the integration and modelling of multiple data modalities, including various omics data sources, clinical data, real-world data, image data and more, in order to make use of the strengths of each data type, while mitigating limitations arising from using a single data source.
What do we offer?
We use multiple data sources to provide a holistic approach to data learning by developing learning models that effectively integrate different data features simultaneously. By doing so, we can discover trends hidden in multiple data sources that only become evident when they are included together in the learning process. Switching to multimodal predictions also enables more robust predictions than a unimodal learning system.