ArticleScientific reports2013
Discovering disease-disease associations by fusing systems-level molecular data.
Article in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
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Who cites it
56 citing papers in PubMed.
- The APOA1-SNCA Axis as a Molecular Bridge Between CKD and Parkinson's Disease: A Systems Biology Model of Kidney-to-Brain Propagation via Exosomal Pathways.International journal of molecular sciences · 2026Article
- Disease association study of Autoimmune and autoinflammatory diseases by integrating multi-modal data and hierarchical ontologies.Frontiers in immunology · 2025Article
- KGRDR: a deep learning model based on knowledge graph and graph regularized integration for drug repositioning.Frontiers in pharmacology · 2025Article
- GSK3β serves as a novel therapeutic target in patients with type 2 diabetes mellitus complicated by colorectal cancer.American journal of translational research · 2025Article
- A Multi-Dimensional Approach to Map Disease Relationships Challenges Classical Disease Views.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Re-Addressing Dementia by Network Medicine and Mechanism-Based Molecular Endotypes.Journal of Alzheimer's disease : JAD · 2023Article
- DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding.Frontiers in genetics · 2023Article
- Linear functional organization of the omic embedding space.Bioinformatics (Oxford, England) · 2021Article
- Discovering disease-disease associations using electronic health records in The Guideline Advantage (TGA) dataset.Scientific reports · 2021Article
- SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations.BMC bioinformatics · 2021Article
- Network neighbors of viral targets and differentially expressed genes in COVID-19 are drug target candidates.Scientific reports · 2021Article
- Leveraging network analysis to evaluate biomedical named entity recognition tools.Scientific reports · 2021Article
- Observational
- A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization.Frontiers in genetics · 2021Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
- Using a Large Margin Context-Aware Convolutional Neural Network to Automatically Extract Disease-Disease Association from Literature: Comparative Analytic Study.JMIR medical informatics · 2019Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- Understanding and predicting disease relationships through similarity fusion.Bioinformatics (Oxford, England) · 2019Article
- Navigating the disease landscape: knowledge representations for contextualizing molecular signatures.Briefings in bioinformatics · 2019Review
- Towards a data-integrated cell.Nature communications · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The advent of genome-scale genetic and genomic studies allows new insight into disease classification. Recently, a shift was made from linking diseases simply based on their shared genes towards systems-level integration of molecular data. Here, we aim to find relationships between diseases based on evidence from fusing all available molecular interaction and ontology data. We propose a multi-level hierarchy of disease classes that significantly overlaps with existing disease classification. In it, we find 14 disease-disease associations currently not present in Disease Ontology and provide evidence for their relationships through comorbidity data and literature curation. Interestingly, even though the number of known human genetic interactions is currently very small, we find they are the most important predictor of a link between diseases. Finally, we show that omission of any one of the included data sources reduces prediction quality, further highlighting the importance in the paradigm shift towards systems-level data fusion.
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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.