ArticleJournal of biomedical semantics2011
A shortest-path graph kernel for estimating gene product semantic similarity.
Article in Journal of biomedical semantics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data.Frontiers in neuroscience · 2022Article
- GO functional similarity clustering depends on similarity measure, clustering method, and annotation completeness.BMC bioinformatics · 2019Article
- Article
- Machine Learning Classification Combining Multiple Features of A Hyper-Network of fMRI Data in Alzheimer's Disease.Frontiers in neuroscience · 2017Article
- TopoICSim: a new semantic similarity measure based on gene ontology.BMC bioinformatics · 2016Article
- Topological graph kernel on multiple thresholded functional connectivity networks for mild cognitive impairment classification.Human brain mapping · 2014Article
- Frequent and discriminative subnetwork mining for mild cognitive impairment classification.Brain connectivity · 2014Article
- Information content-based Gene Ontology functional similarity measures: which one to use for a given biological data type?PloS one · 2014Article
- Information content-based gene ontology semantic similarity approaches: toward a unified framework theory.BioMed research international · 2013Article
- A topology-based metric for measuring term similarity in the gene ontology.Advances in bioinformatics · 2012Article
- Disjunctive shared information between ontology concepts: application to Gene Ontology.Journal of biomedical semantics · 2011Article
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Authors and funding
3 authors.
Funding
Abstract
backgroundExisting methods for calculating semantic similarity between gene products using the Gene Ontology (GO) often rely on external resources, which are not part of the ontology. Consequently, changes in these external resources like biased term distribution caused by shifting of hot research topics, will affect the calculation of semantic similarity. One way to avoid this problem is to use semantic methods that are "intrinsic" to the ontology, i.e. independent of external knowledge.
resultsWe present a shortest-path graph kernel (spgk) method that relies exclusively on the GO and its structure. In spgk, a gene product is represented by an induced subgraph of the GO, which consists of all the GO terms annotating it. Then a shortest-path graph kernel is used to compute the similarity between two graphs. In a comprehensive evaluation using a benchmark dataset, spgk compares favorably with other methods that depend on external resources. Compared with simUI, a method that is also intrinsic to GO, spgk achieves slightly better results on the benchmark dataset. Statistical tests show that the improvement is significant when the resolution and EC similarity correlation coefficient are used to measure the performance, but is insignificant when the Pfam similarity correlation coefficient is used.
conclusionsSpgk uses a graph kernel method in polynomial time to exploit the structure of the GO to calculate semantic similarity between gene products. It provides an alternative to both methods that use external resources and "intrinsic" methods with comparable performance.
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