Fiche publication
Date publication
août 2019
Journal
Scientific data
Auteurs
Membres identifiés du Cancéropôle Est :
Dr HERAULT Yann
Tous les auteurs :
Sügis E, Dauvillier J, Leontjeva A, Adler P, Hindie V, Moncion T, Collura V, Daudin R, Loe-Mie Y, Herault Y, Lambert JC, Hermjakob H, Pupko T, Rain JC, Xenarios I, Vilo J, Simonneau M, Peterson H
Lien Pubmed
Résumé
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 different 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.
Référence
Sci Data. 2019 Aug 14;6(1):151
