ArticleBMC bioinformatics2018
A new method to measure the semantic similarity from query phenotypic abnormalities to diseases based on the human phenotype ontology.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- An epidemiological knowledge graph extracted from the World Health Organization's Disease Outbreak News.Scientific data · 2025Article
- A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.NPJ digital medicine · 2025Article
- Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond.BMC bioinformatics · 2024Article
- diseaseGPS: auxiliary diagnostic system for genetic disorders based on genotype and phenotype.Bioinformatics (Oxford, England) · 2023Article
- Enriching representation learning using 53 million patient notes through human phenotype ontology embedding.Artificial intelligence in medicine · 2023Article
- A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain.Database : the journal of biological databases and curation · 2020Article
- HPO2Vec+: Leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology.Journal of biomedical informatics · 2019Article
- VCF-Server: A web-based visualization tool for high-throughput variant data mining and management.Molecular genetics & genomic medicine · 2019Article
- Functional Site Discovery From Incomplete Training Data: A Case Study With Nucleic Acid-Binding Proteins.Frontiers in genetics · 2019Article
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4 authors.
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Abstract
backgroundAlthough rapid developed sequencing technologies make it possible for genotype data to be used in clinical diagnosis, it is still challenging for clinicians to understand the results of sequencing and make correct judgement based on them. Before this, diagnosis based on clinical features held a leading position. With the establishment of the Human Phenotype Ontology (HPO) and the enrichment of phenotype-disease annotations, there throws much more attention to the improvement of phenotype-based diagnosis.
resultsIn this study, we presented a novel method called RelativeBestPair to measure similarity from the query terms to hereditary diseases based on HPO and then rank the candidate diseases. To evaluate the performance, we simulated a set of patients based on 44 complex diseases. Besides, by adding noise or imprecision or both, cases closer to real clinical conditions were generated. Thus, four simulated datasets were used to make comparison among RelativeBestPair and seven existing semantic similarity measures. RelativeBestPair ranked the underlying disease as top 1 on 93.73% of the simulated dataset without noise and imprecision, 93.64% of the simulated dataset with noise and without imprecision, 39.82% of the simulated dataset without noise and with imprecision, and 33.64% of the simulated dataset with both noise and imprecision.
conclusionCompared with the seven existing semantic similarity measures, RelativeBestPair showed similar performance in two datasets without imprecision. While RelativeBestPair appeared to be equal to Resnik and better than other six methods in the simulated dataset without noise and with imprecision, it significantly outperformed all other seven methods in the simulated dataset with both noise and imprecision. It can be indicated that RelativeBestPair might be of great help in clinical setting.
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