Improving prediction and prognosis of multiple sclerosis; algorithmic approaches and biomarkers

Multiple Sclerosis (MS), a complex autoimmune disorder affecting mainly young adults, with a higher prevalence in women.

Its diverse symptoms and inherent progression complexity hinder early detection and personalized prognostic evaluations.

This proposal aspires to enhance health outcomes, elucidate MS’s intricate mechanisms, and reduce neurological disabilities through innovative, transformative methodologies.The objective is to develop advanced predictive models by harnessing extensive multimodal data, facilitating early detection, and personalized interventions.

The project is organized in 6 different but interconnected tasks and will run for 5-years. We will integrate clinical, lifestyle, and ´omics´ data.

By targeting the prodromal phase of MS, we aspire to devise impactful intervention strategies and project disease progression, with Artificial Intelligence (AI) serving as the cornerstone for analyzing data. We will test the MS prediction among first degree relatives. We will also identify biomarkers for MS risk and severity and genetic factors associated with MS severity.

A special focus will be on role of sex hormones.Employing advanced analytical techniques, network theories, autoencoders and deep learning, we will probe the multifaceted nature of MS using one of the world’s leading MS-specific databases. This will contribute to more comprehensive and individualized MS treatment approaches.