Clustering of Alzheimer's and Parkinson's disease based on genetic burden of shared molecular mechanisms.

ABSTRACT: One of the visions of precision medicine has been to re-define disease taxonomies based on molecular characteristics rather than on phenotypic evidence. However, achieving this goal is highly challenging, specifically in neurology. Our contribution is a machine-learning based joint molecular subtyping of Alzheimer's (AD) and Parkinson's Disease (PD), based on the genetic burden of 15 molecular mechanisms comprising 27 proteins (e.g. APOE) that have been described in both diseases. We demonstrate that our joint AD/PD clustering using a combination of sparse autoencoders and sparse non-negative matrix factorization is reproducible and can be associated with significant differences of AD and PD patient subgroups on a clinical, pathophysiological and molecular level. Hence, clusters are diseas

SUBMITTER: Emon MA 

PROVIDER: S-EPMC7645798 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature