ArticleEuropean journal of human genetics : EJHG2011
The expanded human disease network combining protein-protein interaction information.
Article in European journal of human genetics : EJHG, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Precision Medicine Through Network Language: Integrating Clinical Insight and Data Expertise.Genes · 2026Review
- Mendelian randomization reveals plasminogen as a common therapeutic target for myocardial infarction and atrial fibrillation.Journal of cardiovascular and thoracic research · 2024Article
- Discovering potential interactions between rare diseases and COVID-19 by combining mechanistic models of viral infection with statistical modeling.Human molecular genetics · 2022Article
- Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment.Briefings in bioinformatics · 2021Article
- Epidemiological Characterization of a Directed and Weighted Disease Network Using Data From a Cohort of One Million Patients: Network Analysis.Journal of medical Internet research · 2020Article
- MultiSourcDSim: an integrated approach for exploring disease similarity.BMC medical informatics and decision making · 2019Article
- Computational Methods for Identifying Similar Diseases.Molecular therapy. Nucleic acids · 2019Review
- The translational network for metabolic disease - from protein interaction to disease co-occurrence.BMC bioinformatics · 2019Article
- Network Medicine in the Age of Biomedical Big Data.Frontiers in genetics · 2019Review
- Applications of molecular networks in biomedicine.Biology methods & protocols · 2019Review
- Article
- Mechanistic and topological explanations in medicine: the case of medical genetics and network medicine.Synthese · 2018Article
- Disease causality extraction based on lexical semantics and document-clause frequency from biomedical literature.BMC medical informatics and decision making · 2017Article
- Network mirroring for drug repositioning.BMC medical informatics and decision making · 2017Article
- Fusing literature and full network data improves disease similarity computation.BMC bioinformatics · 2016Article
- Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules.BMC bioinformatics · 2016Article
- Deciphering the Potential Pharmaceutical Mechanism of Chinese Traditional Medicine (Gui-Zhi-Shao-Yao-Zhi-Mu) on Rheumatoid Arthritis.Scientific reports · 2016Observational
- Mechanism-based disease similarity.Journal of rare diseases research & treatment · 2016Article
- Systematic large-scale study of the inheritance mode of Mendelian disorders provides new insight into human diseasome.European journal of human genetics : EJHG · 2014Article
- SemFunSim: a new method for measuring disease similarity by integrating semantic and gene functional association.PloS one · 2014Article
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Authors and funding
8 authors.
Funding
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Abstract
The human disease network (HDN) has become a powerful tool for revealing disease-disease associations. Some studies have shown that genes that share similar or same disease phenotypes tend to encode proteins that interact with each other. Therefore, protein-protein interactions (PPIs) may help us to further understand the relationships between diseases with overlapping clinical phenotypes. In this study, we constructed the expanded HDN (eHDN) by combining disease gene information with PPI information, and analyzed its topological features and functional properties. We found that the network is hierarchical and, most diseases are connected to only a few diseases, whereas a small part of diseases are linked to many different diseases. Diseases in a specific disease class tend to cluster together, and genes associated with the same disease are functionally related. Comparing the eHDN with the original HDN (oHDN, constructed using disease gene information) revealed high consistency over all topological and functional properties. This, to some extent, indicates that our eHDN is reliable. In the eHDN, we found some new associations among diseases resulting from the shared genes interacting with disease genes. The new eHDN will provide a valuable reference for clinicians and medical researchers.
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