ArticleScientific reports2015
Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 1 of them a synthesis that pooled it.
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Who cites it
46 citing papers in PubMed, 1 synthesis or guideline pooled it, 104 citations in OpenAlex.
- Natural language processing systems for capturing and standardizing unstructured clinical information: A systematic review.Journal of biomedical informatics · 2017Pooled it
- Finding the most promising indications for novel treatments in oncology.NPJ precision oncology · 2026Article
- Talking about diseases; developing a model of patient and public-prioritised disease phenotypes.NPJ digital medicine · 2024Article
- Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation.Academic radiology · 2023Review
- A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs).Academic radiology · 2023Review
- PheSom: a term frequency-based method for measuring human phenotype similarity on the basis of MeSH vocabulary.Frontiers in genetics · 2023Article
- Network medicine: facilitating a new view on complex diseases.Frontiers in bioinformatics · 2023Article
- Exploring novel disease-disease associations based on multi-view fusion network.Computational and structural biotechnology journal · 2023Article
- Evaluating semantic similarity methods for comparison of text-derived phenotype profiles.BMC medical informatics and decision making · 2022Article
- Article
- Multi-faceted semantic clustering with text-derived phenotypes.Computers in biology and medicine · 2021Article
- SicknessMiner: a deep-learning-driven text-mining tool to abridge disease-disease associations.BMC bioinformatics · 2021Article
- Linking common human diseases to their phenotypes; development of a resource for human phenomics.Journal of biomedical semantics · 2021Article
- Towards similarity-based differential diagnostics for common diseases.Computers in biology and medicine · 2021Article
- The similarity of inherited diseases (I): clinical similarity within the phenotypic series.BMC medical genomics · 2021Article
- Effects of Negation and Uncertainty Stratification on Text-Derived Patient Profile Similarity.Frontiers in digital health · 2021Article
- Application and evaluation of knowledge graph embeddings in biomedical data.PeerJ. Computer science · 2021Article
- DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.PLoS computational biology · 2020Article
- Phenotypically Similar Rare Disease Identification from an Integrative Knowledge Graph for Data Harmonization: Preliminary Study.JMIR medical informatics · 2020Article
- The similarity of inherited diseases (II): clinical and biological similarity between the phenotypic series.BMC medical genomics · 2020Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Phenotypes are the observable characteristics of an organism arising from its response to the environment. Phenotypes associated with engineered and natural genetic variation are widely recorded using phenotype ontologies in model organisms, as are signs and symptoms of human Mendelian diseases in databases such as OMIM and Orphanet. Exploiting these resources, several computational methods have been developed for integration and analysis of phenotype data to identify the genetic etiology of diseases or suggest plausible interventions. A similar resource would be highly useful not only for rare and Mendelian diseases, but also for common, complex and infectious diseases. We apply a semantic text-mining approach to identify the phenotypes (signs and symptoms) associated with over 6,000 diseases. We evaluate our text-mined phenotypes by demonstrating that they can correctly identify known disease-associated genes in mice and humans with high accuracy. Using a phenotypic similarity measure, we generate a human disease network in which diseases that have similar signs and symptoms cluster together, and we use this network to identify closely related diseases based on common etiological, anatomical as well as physiological underpinnings.
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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.