ArticleJournal of translational medicine2014
Unraveling human protein interaction networks underlying co-occurrences of diseases and pathological conditions.
Article in Journal of translational medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 29 citations in OpenAlex.
- Applications of network analysis to routinely collected health care data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
- Application of knowledge graphs in rare disease research.Frontiers in public health · 2026Review
- Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets.Molecular therapy. Nucleic acids · 2020Article
- Prediction of comorbid diseases using weighted geometric embedding of human interactome.BMC medical genomics · 2019Article
- The translational network for metabolic disease - from protein interaction to disease co-occurrence.BMC bioinformatics · 2019Article
- Protein-Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome.International journal of molecular sciences · 2019Article
- Functional diversity of topological modules in human protein-protein interaction networks.Scientific reports · 2017Article
- OCDD: an obesity and co-morbid disease database.BioData mining · 2017Article
- Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records.Yearbook of medical informatics · 2016Review
- Repurpose terbutaline sulfate for amyotrophic lateral sclerosis using electronic medical records.Scientific reports · 2015Article
- Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation.Avicenna journal of medical biotechnologyArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundHuman diseases frequently cause complications such as obesity-induced diabetes and share numbers of pathological conditions, such as inflammation, by dysfunctions of common functional modules, such as protein-protein interactions (PPIs).
methodsOur developed pipeline, ICod (Interaction analysis for disease Comorbidity), grades similarities between pairs of disease-related PPIs including comorbid diseases and pathological conditions. ICod displayed a disease similarity network consisting of nodes of disease PPIs and edges of similarity value. As a proof of concept, eight complex diseases and pathological conditions, such as type 2 diabetes, obesity, inflammation, and cancers, were examined to discover whether PPIs shared between diseases were associated with comorbidities.
resultsBy comparing Medicare reports of disease co-occurrences from 31 million patients, the disease similarity network shows that PPIs of pathological conditions, including insulin resistance, and inflammation, overlap significantly with PPIs of various comorbid diseases, including diabetes, obesity, and cancers (p < 0.05). Interestingly, maintaining connectivity between essential genes was more drastically perturbed by removing a node of a disease-related gene rather than a pathological condition-related gene, such as one related to inflammations.
conclusionThus, PPIs of pathological symptoms are underlying functional modules across diseases accompanying comorbidity phenomena, whereas they contribute only marginally to maintaining interactions between essential genes.
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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 Socratic graph.