ArticleScientific data2019
HENA, heterogeneous network-based data set for Alzheimer's disease.
Article in Scientific data, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 54 citations in OpenAlex.
- Machine Learning Approaches for Optimizing Drug Combinations in Neurodegenerative Diseases: A Brief Review.ACS omega · 2025Review
- The Alzheimer's Knowledge Base: A Knowledge Graph for Alzheimer Disease Research.Journal of medical Internet research · 2024Article
- Serum proteomics revealsmedRxiv : the preprint server for health sciences · 2023Article
- Recent Advances in Molecular and Cellular Functions of S100A10.Biomolecules · 2023Review
- Applications of multi-omics analysis in human diseases.MedComm · 2023Review
- Discovery of drug lead compounds for Anti-Alzheimer's disease on the basis of synaptic plasticity.Heliyon · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.Research square · 2023Article
- From Answers to Insights: Unveiling the Strengths and Limitations of ChatGPT and Biomedical Knowledge Graphs.medRxiv : the preprint server for health sciences · 2023Article
- Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Synthesize heterogeneous biological knowledge via representation learning for Alzheimer's disease drug repurposing.iScience · 2023Article
- Single-cell-led drug repurposing for Alzheimer's disease.Scientific reports · 2023Article
- Heterogeneous graph construction and HinSAGE learning from electronic medical records.Scientific reports · 2022Article
- TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models.Scientific data · 2022Article
- New Drug Design Avenues Targeting Alzheimer's Disease by Pharmacoinformatics-Aided Tools.Pharmaceutics · 2022Review
- Multi-Omics-Based Autophagy-Related Untypical Subtypes in Patients with Cerebral Amyloid Pathology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Article
- Multi-modal intermediate integrative methods in neuropsychiatric disorders: A review.Computational and structural biotechnology journal · 2022Review
- Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data.Emerging topics in life sciences · 2021Review
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- Multi-omics integration in biomedical research - A metabolomics-centric review.Analytica chimica acta · 2021Review
Corrections and comments
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Authors and funding
18 authors at 10 institutions in 6 countries.
Funding
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.