Multimodal data

…mathematical models combined with multimodal data. Using influenza, the seminar shows how models can expose biological processes that measurements alone do not reveal, explain why similar biomarker profiles can reflect different disease states, and improve predictions of outcomes. The approach links otherwise inaccessible mechanisms to a foundation for predictive, mechanism-based precision medicine.

…experimental proteomics, large-scale multimodal data integration, computational and statistical analysis, and collaborative open-science resource development.

Systems neuroscience experiments generate multimodal data streams, including neuronal activity, stimuli, movement, and behavior events. In analysis, it can be beneficial to avoid hypothesis-driven models and instead take a data-driven approach. For this, we need a general purpose framework to handle the diverse data, and identify relationships across space, time, and modalities in a computationally efficient manner. In this work, we introduce such a framework. Our model, called the Neuroformer, can simulate neuronal populations, and jointly process an arbitrary number of additional modalities

…To integrate multimodal data and benefit from these complementary measurements, I will describe an approach that considers how di

…modelling approach that mines multimodal data from patients at early dementia stages to derive individualised prognostic scores of cognitive decline Our approach has potential to facilitate effective stratification of individuals based on prognostic disease trajectories, reducing patient misclassification with important implications for clinical practice.