The need for integrative approaches to provide a broader understanding of brain related pathologies, in general, and neurogenerative diseases, in particular, has been largely recognised. These approaches should infer relationships between omics, clinical, and personal data. In NEUROCLINOMICS, we are interested in the development of innovative approaches to understanding neurodegenerative diseases through heterogeneous data integration. We work on the development of a sophisticated knowledge discovery system to integrate powerful data mining algorithms to unravel potentially relevant links between omics and clinical data. Disease diagnostic and prognostic markers, disease progression rates, and patient profiles, are tackled. Together with the challenging task of studying complex diseases we also embrace the challenging topic of developing efficient and effective mining algorithms for biomedical data integration. We now use Amyotrophic Lateral Sclerosis and Alzheimer's disease as case studies.