Methods and Models for Multimodal and Multi-scale
Data Integration

Project context

The M4DI project was created in the framework of the PEPR Santé Numérique. This project gathers a large consortium composed of 13 French laboratories with interdisciplinary expertise in mathematics, computer sciences and biomedicine.

Project abstract

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 are further organized in 3 main task forces:


Multi-omics integration

Different methods will be developed, based on statistics, networks and/or deep learning or combination thereof, both supervised and unsupervised

Prior knowledge

Use of prior knowledge

The methods proposed here will leverage prior knowledge with the objective of better inferring cellular heterogeneity, better represent patients and better define health events.

Health databases

Exploration of health databases

This TaskForce will examine different aspects of data integration using data from health databases, including defining disease phenotypes and trajectories, establishing distributed protocols, or linking phenotypes with genotypes

There three task forces will be completed by transversal task forces dedicated to:

Biases and interpretation

Biases and interpretation

Developing methods based on artificial intelligence in biomedicine raises several specific concerns regarding potential biases and lack of interpretability of the models. Importantly, as these issues concern the field of artificial intelligence for digital health at large, all the IRPs are associated with this task force.


Benchmarking of the methods

Benchmarking artificial intelligence and numeric methods is fundamental to compare the performances and features of the different frameworks. This task force will examine the strategies allowing a proper assessment of multimodal data integration approaches, to determine the most effective and efficient.

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