HENA, heterogeneous networkbased data set for Alzheimer’s disease

Anna Leontjeva

Abstract:

Alzheimer’s disease and other types of dementia are the top cause for disabilities in later life and various
types of experiments have been performed to understand the underlying mechanisms of the disease
with the aim of coming up with potential drug targets. These experiments have been carried out by
scientists working in diferent domains such as proteomics, molecular biology, clinical diagnostics and
genomics. The results of such experiments are stored in the databases designed for collecting data of
similar types. However, in order to get a systematic view of the disease from these independent but
complementary data sets, it is necessary to combine them. In this study we describe a heterogeneous
network-based data set for Alzheimer’s disease (HENA). Additionally, we demonstrate the application
of state-of-the-art graph convolutional networks, i.e. deep learning methods for the analysis of such
large heterogeneous biological data sets. We expect HENA to allow scientists to explore and analyze
their own results in the broader context of Alzheimer’s disease research.