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Sci Data. 2019 Aug 14;6(1):151. doi: 10.1038/s41597-019-0152-0.

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

Author information

1
Quretec Ltd., Ülikooli 6a, 51003, Tartu, Estonia.
2
Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia.
3
Swiss Institute of Bioinformatics, Vital-IT group, Unil Quartier Sorge, Genopode building, CH-1015, Lausanne, Switzerland.
4
CSIRO Data 61, 5/13 Garden St, Eveleigh, NSW, 2015, Australia.
5
Hybrigenics SA, 3-5 Impasse Reille, 75014, Paris, France.
6
Institut national de la santé et de la recherche médicale, INSERM U894 2 ter rue d'Alésia, 75014, Paris, France.
7
Laboratoire Aimé Cotton, Centre National Recherche Scientifique, Université Paris-Sud, Ecole Normale Supérieure Paris-Saclay, Université Paris-Saclay, 91405, Orsay, France.
8
(Epi)genomics of Animal Development Unit, Institut Pasteur, CNRS UMR3738, Paris, 75015, France.
9
Centre Européen de Recherche en Biologie et Médecine, 1 rue Laurent Fries, 67404, Illkirch, France.
10
Institut Pasteur de Lille, UMR 744 1 rue du Pr. Calmette BP 245, 59019, Lille cedex, France.
11
European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, CB10 1SD, Hinxton, United Kingdom.
12
George S. Wise Faculty of Life Sciences, School of Molecular Cell Biology and Biotechnology, Tel Aviv University, P.O. Box 39040, 6997801, Tel Aviv, Israel.
13
Center for Integrative Genomics University of Lausanne, Genopode, 1015, Lausanne, Switzerland.
14
Genome Center Health 2030, Analytical Platform Department, Chemin des Mines 9, 1202, Genève, Switzerland.
15
DFR CHUV, Rue du Bugnon 21, 1011, Lausanne, Switzerland.
16
Agora Center, LICR/Department of Oncology, Rue du Bugnon 25A, 1005, Lausanne, Switzerland.
17
Institut national de la santé et de la recherche médicale, INSERM U894 2 ter rue d'Alésia, 75014, Paris, France. michel.simonneau@ens-paris-saclay.fr.
18
Laboratoire Aimé Cotton, Centre National Recherche Scientifique, Université Paris-Sud, Ecole Normale Supérieure Paris-Saclay, Université Paris-Saclay, 91405, Orsay, France. michel.simonneau@ens-paris-saclay.fr.
19
Quretec Ltd., Ülikooli 6a, 51003, Tartu, Estonia. peterson@quretec.com.
20
Institute of Computer Science, University of Tartu, J. Liivi 2, 50409, Tartu, Estonia. peterson@quretec.com.

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 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.

Publication type, Grant support

Publication type

Grant support

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