Project "MitoIntegrOMICS"

Mitochondrial diseases (MD) are rare disorders caused by deficiency of the mitochondrial respiratory chain, which provides energy in each cell. MD are caused by alterations (variants) on genes involved in mitochondrial functions. The diagnosis of MD is based on the identification of the disease responsible gene(s), that will allow to be able to offer genetic counseling, prenatal diagnosis, to consider therapeutic approaches and to improve the care of patients. Nowadays, technologies currently used for detecting causal variants is far from complete, ranging from 25 to 50%.
 

To address these needs our research teams propose to gather three different domains: medical, bioinformatic and machine learning, in order to set up an integrated multi-omics approach to identify novel causal variants. We foresee that this project will contribute to set up new diagnostic tools to reduce the number of patients with a diagnostic stalemate. This study will settle the milestones to transfer the conjoint use of multi-omics technologies from research fields to diagnostic environment.

The project is mainly composed by three steps, specifically the doctoral candidate will: 1) perform bioinformatic analysis of multi-omics data; 2) develop a deep-learning multi-integromics approach; 3) implement a new variants prioritization AI algorithm.

This project will allow to develop novel algorithms that will found application not only in MD diagnostic, but also in other genetic disorders and cancer, to allow the development of personalized medicine to ameliorate patients healthcare. We foresee that this project will provide a product easily transferable to non-academic field and easily employed in medical environment and several industrial sectors. Importantly, the fellow will gain outstanding competences in data science, an exponentially growing field in high demand in any field within and outside academia. In support of that, the intervention of the company “MyDataModels” in the current project will facilitate and enhance the integration of the fellows into non academic environment.

• Michel, Riveill, Laboratoire I3S (UMR 7271 CNRS UNS)
• Silvia, Bottini, directrice operationelle of Medical Data Laboratory (MDLab), Maison de la Modelisation, de la Simulation et des Interactions (MSI)
• Véronique, Paquis, PU-PH, CHU de Nice, IRCAN UMR CNRS 7284/INSERM U1081