The motivation for NEUROCLINOMICS (PTDC/EIA-EIA/111239/2009) was the largely recognized need for integrative approaches allowing a broader study of brain related pathologies. Its contribution emphasized the relevance of inferring relationships within and between heterogeneous sources of omic, clinical, and personal data. In this project, we developed the prototype of a knowledge discovery (KD) system that currently integrates data mining algorithms to unravel biomedical markers. In NEUROCLINOMICS2, we move towards the challenging tasks of understanding disease progression patterns and predicting prognostic markers for personalized medicine. We now focus on learning prognostic markers through the analysis of multivariate time series, where time points are snapshots of the patient´s condition collected periodically along their follow up. Addressing these challenges will result in new mining algorithms to be integrated in the KD system, which will be upgraded for both desktop and mobile platforms, and continuously updated with new national data from national data from patients follow up and relevant international data from ADNI and ProACT.
LASIGE is supported by FCT, project UID/CEC/00408/2019