Anaïs Baudot
Multimodal data integration for rare genetic diseases
Recent technological advances and the growing availability of biomedical datasets offer unprecedented opportunities to better understand human diseases. However, translating the sheer volume and heterogeneity of these data into meaningful insights requires proper computational strategies. In this talk, I will present different approaches for the integrated exploration of heterogeneous datasets, from walking on multilayer networks to knowledge graph embedding to joint dimensionality reduction. I will illustrate the application of these algorithms in the context of rare genetic diseases analysis, which raise various challenges: many patients remain undiagnosed, phenotypes can be highly heterogeneous, and few treatments exist.
Harold Duruflé
Closing the loop between prediction and integration in multi-omics
Complex phenotypic traits are influenced by the interaction of multiple genetic and environmental factors, often regulated by nonlinear interactions. Deciphering these interactions between different endophenotypic spaces, such as epigenomics and transcriptomics, remains a major challenge in biology. In this presentation, I will share the results of integrative and predictive analyses conducted on a large panel of black poplar individuals from natural populations, for which comprehensive multi-omics datasets were generated. By using prediction models integrating multi-omics data, we improve our ability to dissect the relationships between these types of data and clarify the contribution of each omics layer to trait prediction.
Gabriel Krouk
Data integration for plant gene regulatory network modeling… and beyond
In this short presentation, I will share a few snapshots of our ongoing efforts in data integration. These range from modeling Gene Regulatory Networks (GRNs) to leveraging generative AI for the design of regulatory peptides, and perhaps even a drizzle of a new kind of GWAS
Lorenzo Sala
Hybrid data integration with PINNs: mechanistic modeling of biological systems using omics data
Understanding complex biological systems, like microbial communities or plant-pathogen interactions, demands effective ways to integrate omics data with mechanistic models. A key challenge is getting robust parameter estimates from these datasets. In this presentation, I'll share how we're tackling this using a hybrid data integration framework that leverages Physics-Informed Neural Networks (PINNs). We embed various Ordinary Differential Equation (ODE) models into these networks, allowing us to fuse mechanistic knowledge with observational omics data. I'll show how this approach enables more robust and interpretable parameter inferences, even when dealing with noisy and sparse datasets. This strategy holds significant promise for effectively scaling analyses with the increasing volume and complexity of diverse omics data, laying groundwork for future multi-omic integrations.