ArticlePLoS computational biology2017
Semantic prioritization of novel causative genomic variants.
Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
22 citing papers in PubMed, 37 citations in OpenAlex.
- Ontology-driven association rule mining for biomedical entity relationships: integrating hierarchical knowledge to improve gene-disease discovery.Scientific reports · 2026Article
- Towards a standard benchmark for phenotype-driven variant and gene prioritisation algorithms: PhEval - Phenotypic inference Evaluation framework.BMC bioinformatics · 2025Article
- Bridging mouse and human anatomies; a knowledge-based approach to comparative anatomy for disease model phenotyping.Mammalian genome : official journal of the International Mammalian Genome Society · 2023Review
- Phenotype-aware prioritisation of rare Mendelian disease variants.Trends in genetics : TIG · 2022Review
- Contribution of model organism phenotypes to the computational identification of human disease genes.Disease models & mechanisms · 2022Article
- Review
- Predicting candidate genes from phenotypes, functions and anatomical site of expression.Bioinformatics (Oxford, England) · 2021Article
- CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations.Genome medicine · 2020Article
- Genetic Modifiers and Rare Mendelian Disease.Genes · 2020Review
- A Case of Papillary Thyroid Carcinoma and Kostmann Syndrome: A Genomic Theranostic Approach for Comprehensive Treatment.The American journal of case reports · 2019Article
- Rapid and accurate interpretation of clinical exomes using Phenoxome: a computational phenotype-driven approach.European journal of human genetics : EJHG · 2019Article
- DeepPVP: phenotype-based prioritization of causative variants using deep learning.BMC bioinformatics · 2019Article
- Ontology based text mining of gene-phenotype associations: application to candidate gene prediction.Database : the journal of biological databases and curation · 2019Article
- OligoPVP: Phenotype-driven analysis of individual genomic information to prioritize oligogenic disease variants.Scientific reports · 2018Article
- DES-Mutation: System for Exploring Links of Mutations and Diseases.Scientific reports · 2018Article
- Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes.Bioinformatics (Oxford, England) · 2018Article
- Ontology-based validation and identification of regulatory phenotypes.Bioinformatics (Oxford, England) · 2018Article
- As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018.Yearbook of medical informatics · 2018Review
- Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.Bioinformatics (Oxford, England) · 2018Article
- The VAAST Variant Prioritizer (VVP): ultrafast, easy to use whole genome variant prioritization tool.BMC bioinformatics · 2018Article
Corrections and comments
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Authors and funding
10 authors at 4 institutions in 2 countries.
Funding
Abstract
Discriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.
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Registered trials
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.