ArticleBMC bioinformatics2014
Clinical phenotype-based gene prioritization: an initial study using semantic similarity and the human phenotype ontology.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Diagnostic Yield After Postnatal Reanalysis of Prenatal Exome Sequencing Results.Prenatal diagnosis · 2026Article
- Insights Into Congenital Lymphatic Anomalies Underlying Fetal Effusions.Prenatal diagnosis · 2026Article
- Artificial intelligence-assisted clinical exome sequencing: Insights and outcomes from 822 pediatric diagnoses.Genetics in medicine open · 2026Article
- Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond.BMC bioinformatics · 2024Article
- Automated Shared Phenotype Discovery in Undiagnosed Cohorts for Rare Disease Research.Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications · 2024Article
- Individualised human phenotype ontology gene panels improve clinical whole exome and genome sequencing analytical efficacy in a cohort of developmental and epileptic encephalopathies.Molecular genetics & genomic medicine · 2023Article
- Enriching representation learning using 53 million patient notes through human phenotype ontology embedding.Artificial intelligence in medicine · 2023Article
- The utility of pathologic examination and comprehensive phenotyping for accurate diagnosis with perinatal exome sequencing.Prenatal diagnosis · 2022Article
- Systematic use of phenotype evidence in clinical genetic testing reduces the frequency of variants of uncertain significance.American journal of medical genetics. Part A · 2022Article
- Deepening the knowledge of rare diseases dependent on angiogenesis through semantic similarity clustering and network analysis.Briefings in bioinformatics · 2022Article
- GCSENet: A GCN, CNN and SENet ensemble model for microRNA-disease association prediction.PLoS computational biology · 2021Article
- Pathogenic Mutations and Putative Phenotype-Affecting Variants in Polish Myofibrillar Myopathy Patients.Journal of clinical medicine · 2021Article
- The landscape of nutri-informatics: a review of current resources and challenges for integrative nutrition research.Database : the journal of biological databases and curation · 2021Review
- A pipeline-friendly software tool for genome diagnostics to prioritize genes by matching patient symptoms to literature.Advanced genetics (Hoboken, N.J.) · 2020Article
- Exome Sequencing for Prenatal Diagnosis in Nonimmune Hydrops Fetalis.The New England journal of medicine · 2020Article
- Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes.Genetics in medicine : official journal of the American College of Medical Genetics · 2020Article
- 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
- Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.BMC systems biology · 2019Article
- Rapid and accurate interpretation of clinical exomes using Phenoxome: a computational phenotype-driven approach.European journal of human genetics : EJHG · 2019Article
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9 authors.
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Abstract
backgroundExome sequencing is a promising method for diagnosing patients with a complex phenotype. However, variant interpretation relative to patient phenotype can be challenging in some scenarios, particularly clinical assessment of rare complex phenotypes. Each patient's sequence reveals many possibly damaging variants that must be individually assessed to establish clear association with patient phenotype. To assist interpretation, we implemented an algorithm that ranks a given set of genes relative to patient phenotype. The algorithm orders genes by the semantic similarity computed between phenotypic descriptors associated with each gene and those describing the patient. Phenotypic descriptor terms are taken from the Human Phenotype Ontology (HPO) and semantic similarity is derived from each term's information content.
resultsModel validation was performed via simulation and with clinical data. We simulated 33 Mendelian diseases with 100 patients per disease. We modeled clinical conditions by adding noise and imprecision, i.e. phenotypic terms unrelated to the disease and terms less specific than the actual disease terms. We ranked the causative gene against all 2488 HPO annotated genes. The median causative gene rank was 1 for the optimal and noise cases, 12 for the imprecision case, and 60 for the imprecision with noise case. Additionally, we examined a clinical cohort of subjects with hearing impairment. The disease gene median rank was 22. However, when also considering the patient's exome data and filtering non-exomic and common variants, the median rank improved to 3.
conclusionsSemantic similarity can rank a causative gene highly within a gene list relative to patient phenotype characteristics, provided that imprecision is mitigated. The clinical case results suggest that phenotype rank combined with variant analysis provides significant improvement over the individual approaches. We expect that this combined prioritization approach may increase accuracy and decrease effort for clinical genetic diagnosis.
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