Supporting data for "A similarity-based approach to leverage multi-cohort medical data on the diagnosis and prognosis of Alzheimer's disease"

Heterogeneous diseases such as Alzheimer's disease manifest a variety of phenotypes among populations. Early diagnosis and effective treatment offer costbenefits. Many studies on biochemical and imaging markers have shown potential promise in improving diagnosis, yet establishing quantitative diagnostic criteria for ancillary tests remains challenging. <br> We have developed a similarity-based approach that matches individuals to subjects with similar conditions. We modeled the disease with a Gaussian process, and tested the method in the Alzheimer's Disease Big Data DREAM Challenge. Ranked the highest among submitted methods, our diagnostic model predicted cognitive impairment scores in an independent dataset test with a correlation score of 0.573. It differentiated Alzheimer's disease patients from control subjects with an area under receiver operating curve of 0.920. Without knowing longitudial information about subjects, the model predicted patients that are vulnerable to MCI-to-AD conversion through the similarity network. This diagnostic framework can be applied to other diseases with clinical heterogeneity, such as Parkinson's disease.