TY - JOUR
T1 - HENA, heterogeneous network-based data set for Alzheimer’s disease
AU - Sügis, Elena
AU - Dauvillier, Jerome
AU - Leontjeva, Anna
AU - Adler, Priit
AU - Hindie, Valerie
AU - Moncion, Thomas
AU - Collura, Vincent
AU - Daudin, Rachel
AU - Loe-Mie, Yann
AU - Herault, Yann
AU - Lambert, Jean Charles
AU - Hermjakob, Henning
AU - Pupko, Tal
AU - Rain, Jean Christophe
AU - Xenarios, Ioannis
AU - Vilo, Jaak
AU - Simonneau, Michel
AU - Peterson, Hedi
N1 - Publisher Copyright:
© 2019, The Author(s).
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85071280143&partnerID=8YFLogxK
U2 - 10.1038/s41597-019-0152-0
DO - 10.1038/s41597-019-0152-0
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C2 - 31413325
AN - SCOPUS:85071280143
SN - 2052-4463
VL - 6
JO - Scientific data
JF - Scientific data
IS - 1
M1 - 151
ER -