ArticlePLoS computational biology2015
Finding New Order in Biological Functions from the Network Structure of Gene Annotations.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Generative prediction of causal gene sets responsible for complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- KG-Hub-building and exchanging biological knowledge graphs.Bioinformatics (Oxford, England) · 2023Article
- Patterns of diverse gene functions in genomic neighborhoods predict gene function and phenotype.Scientific reports · 2019Article
- New Statistical Methods for Constructing Robust Differential Correlation Networks to characterize the interactions among microRNAs.Scientific reports · 2019Article
- PathCORE-T: identifying and visualizing globally co-occurring pathways in large transcriptomic compendia.BioData mining · 2018Article
- How can functional annotations be derived from profiles of phenotypic annotations?BMC bioinformatics · 2017Article
- Integrating Information in Biological Ontologies and Molecular Networks to Infer Novel Terms.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
2 authors.
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Abstract
The Gene Ontology (GO) provides biologists with a controlled terminology that describes how genes are associated with functions and how functional terms are related to one another. These term-term relationships encode how scientists conceive the organization of biological functions, and they take the form of a directed acyclic graph (DAG). Here, we propose that the network structure of gene-term annotations made using GO can be employed to establish an alternative approach for grouping functional terms that captures intrinsic functional relationships that are not evident in the hierarchical structure established in the GO DAG. Instead of relying on an externally defined organization for biological functions, our approach connects biological functions together if they are performed by the same genes, as indicated in a compendium of gene annotation data from numerous different sources. We show that grouping terms by this alternate scheme provides a new framework with which to describe and predict the functions of experimentally identified sets of genes.
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