ArticleJournal of biomedical informatics2015
Comparative analysis of a novel disease phenotype network based on clinical manifestations.
Article in Journal of biomedical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Leveraging multiomic approaches to elucidate mechanisms of heterogeneity in Alzheimer's disease: Neuropsychiatric symptoms, co-pathologies, and sex differences.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- A comparison of anatomic and cellular transcriptome structures across 40 human brain diseases.PLoS biology · 2023Article
- Exploring novel disease-disease associations based on multi-view fusion network.Computational and structural biotechnology journal · 2023Article
- Subtypes of relapsing-remitting multiple sclerosis identified by network analysis.Frontiers in digital health · 2022Article
- Leveraging network analysis to evaluate biomedical named entity recognition tools.Scientific reports · 2021Article
- Phenotype-genotype comorbidity analysis of patients with rare disorders provides insight into their pathological and molecular bases.PLoS genetics · 2020Article
- AllergyGenDB: A literature and functional annotation-based omics database for allergic diseases.Allergy · 2020Article
- Article
- Computational Methods for Identifying Similar Diseases.Molecular therapy. Nucleic acids · 2019Review
- Analysis of disease organ as a novel phenotype towards disease genetics understanding.Journal of biomedical informatics · 2019Article
- The Alzheimer's comorbidity phenome: mining from a large patient database and phenome-driven genetics prediction.JAMIA open · 2019Article
- Context-sensitive network analysis identifies food metabolites associated with Alzheimer's disease: an exploratory study.BMC medical genomics · 2019Article
- Identifying Common Genes, Cell Types and Brain Regions Between Diseases of the Nervous System.Frontiers in genetics · 2019Article
- Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data.BMC bioinformatics · 2018Article
- Context-sensitive network-based disease genetics prediction and its implications in drug discovery.Bioinformatics (Oxford, England) · 2017Article
- Interrogating Patient-level Genomics and Mouse Phenomics towards Understanding Cytokines in Colorectal Cancer Metastasis.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2017Article
- Efficient Results in Semantic Interoperability for Health Care. Findings from the Section on Knowledge Representation and Management.Yearbook of medical informatics · 2016Article
- Phenome-based gene discovery provides information about Parkinson's disease drug targets.BMC genomics · 2016Article
- Towards precision medicine-based therapies for glioblastoma: interrogating human disease genomics and mouse phenotypes.BMC genomics · 2016Article
- Explore Small Molecule-induced Genome-wide Transcriptional Profiles for Novel Inflammatory Bowel Disease Drug.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2016Article
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Authors and funding
4 authors.
Funding
Abstract
Systems approaches to analyzing disease phenotype networks in combination with protein functional interaction networks have great potential in illuminating disease pathophysiological mechanisms. While many genetic networks are readily available, disease phenotype networks remain largely incomplete. In this study, we built a large-scale Disease Manifestation Network (DMN) from 50,543 highly accurate disease-manifestation semantic relationships in the United Medical Language System (UMLS). Our new phenotype network contains 2305 nodes and 373,527 weighted edges to represent the disease phenotypic similarities. We first compared DMN with the networks representing genetic relationships among diseases, and demonstrated that the phenotype clustering in DMN reflects common disease genetics. Then we compared DMN with a widely-used disease phenotype network in previous gene discovery studies, called mimMiner, which was extracted from the textual descriptions in Online Mendelian Inheritance in Man (OMIM). We demonstrated that DMN contains different knowledge from the existing phenotype data source. Finally, a case study on Marfan syndrome further proved that DMN contains useful information and can provide leads to discover unknown disease causes. Integrating DMN in systems approaches with mimMiner and other data offers the opportunities to predict novel disease genetics. We made DMN publicly available at nlp/case.edu/public/data/DMN.
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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.