Methods and models for multimodal and multi-scale data integration


Coordinator(s) : Anaïs Baudot

Coordinating institution : Aix-Marseille University

Key words

Multiscale multimodal multi-omics data, data integration


Thanks to the large amount of preclinical and clinical datasets produced nowadays, including omics, expert knowledge, or health databases, we have an unprecedented opportunity to advance health care. Importantly, no single dataset can capture the full complexity of human pathologies. Indeed, different data modalities offer multi-scale and complementary information. The joint analysis of heterogeneous datasets is expected to reduce the experimental and biological noise, reveal weak signals, and provide a more complete picture of human pathologies.

However, biomedical datasets are scarce, sparse, and highly heterogeneous. They further exhibit big data-related issues: the number of biomedical variables largely dominates the number of patients or samples. The burden is even stronger when the medical purposes involve few patients, i.e., for rare diseases or stratified medicine. Overall, the proper integration of heterogeneous datasets remains a major scientific challenge. New models and methods are required for improved health care and management.

The main objective of the Methods and Models for Multimodal and Multiscale data integration (M4DI) project is to develop innovative methodological frameworks for the integration of biomedical datasets. We will unroll 8 Individual Research Projects (IRP) gathering interdisciplinary teams around 8 PhD students. The students will conduct their research in a host lab with a long-term research stay in a secondment lab. Each IRP will develop a particular aspect of multimodal data integration, working on different types of data, different methods or algorithms, and different biomedical research questions. In this context, the IRPs (Individual Research Projects) are further organized in task forces, which cover major challenges in multimodal data integration, namely the integration of multi-omics data, the use of prior knowledge and the exploration of health databases. These three task forces will be completed by transversal task forces dedicated to biases and interpretation and benchmarking of the methods. All IRPs will work on concrete medical use-cases for which data are already accessible to the teams. The IRPs will deliver scientific publications but also software/packages and/or guidelines/protocols. An engineer will also be hired to help FAIRify the outputs of the project and diffuse them to the community.

Overall, our new methodological frameworks will be transformational for the development of the next generation of approaches capable of advancing health care and helping to predict disease onset, proper diagnosis, and prognosis, clinical decision support, or for the discovery of new therapeutic paths.

Laboratory or department, team Supervisors

U 1251

Inserm, Aix-Marseille University

UMR 5506

CNRS, Inria, Montpellier University, Paul Valery Montpellier 3 University, Perpignan University

U 1141

Inserm, Paris Cité University

UMR 5219

CNRS, Paul Sabatier Toulouse 3 University, Jean Jaurès Toulouse 2 University, Toulouse Capitole EPE University, INSA, INU Champollion, Ecole d’Economie de Toulouse-TSE

UMR 6625

INSA, CNRS, Rennes 2 University, Rennes University

Institut Agro Rennes-Angers & Inria partners

IRISA – UMR 6074 Team DyLiSS INSA, CNRS, Inria, Bretagne Sud University, Rennes University, ENS, Mines Telecom, IMT Atlantique

Inserm, Institut Agro et Centrale Supélec partners

LBBE – UMR 5558 CNRS, Claude Bernard Lyon 1 University, VetAgro Sup
LORIA – UMR 7503 CNRS, Inria, Lorraine University
CRC – U 1138      Team HEKA, Insitut Imagine Inserm, Inria, Sorbonne University, Paris Cité University
LIS – UMR 7020 CNRS, Aix-Marseille University, Toulon University, Ecole Centrale Marseille
IGDR – UMR 6290 CNRS, Rennes University, Inserm
LIRIS – UMR 5205 CNRS, INSA Lyon, Claude Bernard Lyon 1 University, Lumière Lyon 2 University, Ecole centrale de Lyon
TIMC – UMR 5525 CNRS, Grenoble Alpes University, Vetagro Sup, Grenoble INP partner