ArticlePloS one2011
Exploring and exploiting disease interactions from multi-relational gene and phenotype networks.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 1 of them a synthesis that pooled it.
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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
36 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Applications of network analysis to routinely collected health care data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018Pooled it
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- Exploring the perceptions of patients with chronic respiratory diseases and their insights into pulmonary rehabilitation in Bangladesh.Journal of global health · 2024Article
- Exploring novel disease-disease associations based on multi-view fusion network.Computational and structural biotechnology journal · 2023Article
- The effect of disease co-occurrence measurement on multimorbidity networks: a population-based study.BMC medical research methodology · 2022Article
- Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment.Briefings in bioinformatics · 2021Article
- Approaches to Integrating Metabolomics and Multi-Omics Data: A Primer.Metabolites · 2021Review
- Profiling and analysis of chemical compounds using pointwise mutual information.Journal of cheminformatics · 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
- The translational network for metabolic disease - from protein interaction to disease co-occurrence.BMC bioinformatics · 2019Article
- Debutant iOS app and gene-disease complexities in clinical genomics and precision medicine.Clinical and translational medicine · 2019Article
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- L1000 Viewer: A Search Engine and Web Interface for the LINCS Data Repository.Frontiers in genetics · 2019Article
- Reforming disease classification system-are we there yet?Annals of translational medicine · 2018Article
- Network mirroring for drug repositioning.BMC medical informatics and decision making · 2017Article
- AtPID: a genome-scale resource for genotype-phenotype associations in Arabidopsis.Nucleic acids research · 2017Article
- Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records.Yearbook of medical informatics · 2016Review
- Large-Scale Discovery of Disease-Disease and Disease-Gene Associations.Scientific reports · 2016Article
- Insights into Population Health Management Through Disease Diagnoses Networks.Scientific reports · 2016Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine.
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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.