HENA, heterogeneous network-based data set for Alzheimer’s disease

Elena Sügis (1, 2) , Jerome Dauvillier (3) , Anna Leontjeva (4) , Priit Adler (1, 2) , Valérie Hindie (5) , Thomas Moncion (5) , Vincent Collura (5) , Rachel Daudin (6, 7) , Yann Loe-Mie (8) , Yann Hérault (9, 10) , Jean-Charles Lambert (11) , Henning Hermjakob (12) , Tal Pupko (13) , Jean-Christophe Rain (5) , Ioannis Xenarios (14, 15) , Jaak Vilo (1, 2) , Michel Simonneau (7, 6) , Hedi Peterson (1, 2)

1 Quretec Ltd.
2 Institute of Computer Science [University of Tartu, Estonie]
3 Vital-IT
4 CSIRO - Data61 [Canberra]
5 Hybrigenics [Paris]
6 U894 - Centre de Psychiatrie et Neurosciences
7 LAC - Laboratoire Aimé Cotton
8 Génomique et Epigénomique du Développement des Vertébrés - Genomics and Epigenomics of Vertebrates Development
9 IGBMC - Institut de Génétique et de Biologie Moléculaire et Cellulaire
10 ICS - Institut Clinique de la Souris
11 Epidémiologie des maladies chroniques : impact des interactions gène environnement sur la santé des populations
12 EMBL-EBI - European Bioinformatics Institute [Hinxton]
13 TAU - Tel Aviv University
14 CIG - Center for Integrative Genomics - Institute of Bioinformatics, Génopode
15 CHUV - Centre Hospitalier Universitaire Vaudois = Lausanne University Hospital [Lausanne]

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

Domaines: Génétique; Bio-Informatique, Biologie Systémique [q-bio.QM]

Elena Sügis, Jerome Dauvillier, Anna Leontjeva, Priit Adler, Valérie Hindie, et al.. HENA, heterogeneous network-based data set for Alzheimer’s disease. Scientific Data , 2019, 6 (1), pp.151. ⟨10.1038/s41597-019-0152-0⟩. ⟨hal-02388589⟩