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


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-08-14

AUTHORS

Elena Sügis, Jerome Dauvillier, Anna Leontjeva, Priit Adler, Valerie Hindie, Thomas Moncion, Vincent Collura, Rachel Daudin, Yann Loe-Mie, Yann Herault, Jean-Charles Lambert, Henning Hermjakob, Tal Pupko, Jean-Christophe Rain, Ioannis Xenarios, Jaak Vilo, Michel Simonneau, Hedi Peterson

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. More... »

PAGES

151

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  • Journal

    TITLE

    Scientific Data

    ISSUE

    1

    VOLUME

    6

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41597-019-0152-0

    DOI

    http://dx.doi.org/10.1038/s41597-019-0152-0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1120303603

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/31413325


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