ArticleNucleic acids research2011
PhenomeNET: a whole-phenome approach to disease gene discovery.
Article in Nucleic acids research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 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
130 citing papers in PubMed, 228 citations in OpenAlex.
- ADAM19: biological characteristics and implications in disease pathology and clinical applications.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Review
- INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies.Bioinformatics (Oxford, England) · 2026Article
- Mapping whole-organism genetic comorbidities across model Species using unified ontologies.Genetics · 2026Article
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- The Unified Phenotype Ontology : a framework for cross-species integrative phenomics.Genetics · 2025Article
- Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond.BMC bioinformatics · 2024Article
- The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics.bioRxiv : the preprint server for biology · 2024Article
- Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning.Bioinformatics (Oxford, England) · 2024Article
- SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.Bioinformatics (Oxford, England) · 2023Article
- 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
- Contribution of model organism phenotypes to the computational identification of human disease genes.Disease models & mechanisms · 2022Article
- Evaluating semantic similarity methods for comparison of text-derived phenotype profiles.BMC medical informatics and decision making · 2022Article
- Linking common human diseases to their phenotypes; development of a resource for human phenomics.Journal of biomedical semantics · 2021Article
- Computational Methods for Prediction of Human Protein-Phenotype Associations: A Review.Phenomics (Cham, Switzerland) · 2021Review
- Semantic similarity and machine learning with ontologies.Briefings in bioinformatics · 2021Review
- Predicting candidate genes from phenotypes, functions and anatomical site of expression.Bioinformatics (Oxford, England) · 2021Article
- Improved characterisation of clinical text through ontology-based vocabulary expansion.Journal of biomedical semantics · 2021Article
- Establishment and application of information resource of mutant mice in RIKEN BioResource Research Center.Laboratory animal research · 2021Review
- Transforming the study of organisms: Phenomic data models and knowledge bases.PLoS computational biology · 2020Review
- DeepPheno: Predicting single gene loss-of-function phenotypes using an ontology-aware hierarchical classifier.PLoS computational biology · 2020Article
70 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 2 countries.
Funding
Abstract
Phenotypes are investigated in model organisms to understand and reveal the molecular mechanisms underlying disease. Phenotype ontologies were developed to capture and compare phenotypes within the context of a single species. Recently, these ontologies were augmented with formal class definitions that may be utilized to integrate phenotypic data and enable the direct comparison of phenotypes between different species. We have developed a method to transform phenotype ontologies into a formal representation, combine phenotype ontologies with anatomy ontologies, and apply a measure of semantic similarity to construct the PhenomeNET cross-species phenotype network. We demonstrate that PhenomeNET can identify orthologous genes, genes involved in the same pathway and gene-disease associations through the comparison of mutant phenotypes. We provide evidence that the Adam19 and Fgf15 genes in mice are involved in the tetralogy of Fallot, and, using zebrafish phenotypes, propose the hypothesis that the mammalian homologs of Cx36.7 and Nkx2.5 lie in a pathway controlling cardiac morphogenesis and electrical conductivity which, when defective, cause the tetralogy of Fallot phenotype. Our method implements a whole-phenome approach toward disease gene discovery and can be applied to prioritize genes for rare and orphan diseases for which the molecular basis is unknown.
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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.