ArticleStudies in health technology and informatics2010
A framework for comparing phenotype annotations of orthologous genes.
Article in Studies in health technology and informatics, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- An Evaluation of Machine Learning Approaches for the Prediction of Essential Genes in Eukaryotes Using Protein Sequence-Derived Features.Computational and structural biotechnology journal · 2019Article
- Applications of comparative evolution to human disease genetics.Current opinion in genetics & development · 2015Review
- An integrative approach to ortholog prediction for disease-focused and other functional studies.BMC bioinformatics · 2011Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectivesAnimal models are a key resource for the investigation of human diseases. In contrast to functional annotation, phenotype annotation is less standard, and comparing phenotypes across species remains challenging. The objective of this paper is to propose a framework for comparing phenotype annotations of orthologous genes based on the Medical Subject Headings (MeSH) indexing of biomedical articles in which these genes are discussed.
methods17,769 pairs of orthologous genes (mouse and human) are downloaded from the Mouse Genome Informatics (MGI) system and linked to biomedical articles through Entrez Gene. MeSH index terms corresponding to diseases are extracted from Medline.
results11,111 pairs of genes exhibited at least one phenotype annotation for each gene in the pair. Among these, 81% have at least one phenotype annotation in common, 80% have at least one annotation specific to the human gene and 84% have at least one annotation specific to the mouse gene. Four disease categories represent 54% of all phenotype annotations.
conclusionsThis framework supports the curation of phenotype annotation and the generation of research hypotheses based on comparative studies.
Indexed as
Identifiers
20841896PMC4300101What OpenQuestion holds
Registered trials
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