Data mining has been widely applied in biomarker discovery, resulting in
significant findings of different clinical and biological biomarkers. With
developments in technology, from genomics to proteomics analysis, a deluge
of data has become available, as well as standardized data repositories.
Nonetheless, researchers are still facing important challenges in
analyzing the data, especially when considering the complexity of pathways
involved in biological processes or diseases. Data from single sources
seem unable to explain complex processes, such as the ones involved in
brain related disorders, thus rising the need for a more comprehensive
perspective. A possible solution relies on data and model integration,
where several data types are combined to provide complementary views,
which in turn can result in the discovery of previously unknown
biomarkers, by unravelling otherwise hidden relationships between data of
different sources. In this work, we review the different single-source
types of data used for biomarker discovery in neurodegenerative diseases,
and then proceed to provide an overview on recent efforts to perform
integrative analysis in these disorders, discussing major challenges and
advantages.