MIDAlab
Research Lines
MIDAlab's research interests revolve primarily around the application of data science and machine learning (ML) to the study of metabolism.
In particular, we use metabolomics combined with imaging data to discover diagnostic and prognostic biomarkers for brain diseases.
We use metabolomics together with imaging data to identify diagnostic and prognostic biomarkers for brain diseases. The main focus is on the analysis of magnetic resonance (MR) data, especially magnetic resonance spectroscopy (MRS), either alone or in combination with other MRI-related imaging modalities. Clinical data from collaborating hospitals and clinical centers, as well as preclinical data, are used.
Unlike traditional MRS analysis methodologies based on peak quantification, MIDAlab excels in applying a variety of supervised, unsupervised, and semi-supervised techniques for both classification and discovery of hidden patterns in data.
While the current emphasis is on brain tumors, MIDAlab is expanding its analytical approach to neurodegenerative diseases through scientific collaborations.
STRATEGIC GOALS
MIDAlab’s long-term goal is to bridge the gap between MRS-based scientific findings and their practical application in clinical settings. Our goal is to help clinicians adopt MRS more easily, by developing user-friendly decision support tools (DSS) to unlock the full potential of MRS as a diagnostic modality in clinical practice.
Our main research lines are:
- Using MRS data and images to find diagnostic and prognostic biomarkers in brain tumors
- Application of advanced machine learning (ML) tools to in vivo MRS data for biomarker discovery and automated quality control
- Development and testing of user-friendly software tools for clinical applications
Information of interest
Funding
Additional information
