by Marco Alvarez, Xiaojun Qi, Changhui Yan
Abstract:
BACKGROUND: Existing 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. RESULTS: We 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. CONCLUSIONS: Spgk 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.
Reference:
A shortest-path graph kernel for estimating gene product semantic similarity. (Marco Alvarez, Xiaojun Qi, Changhui Yan), In Journal of biomedical semantics, volume 2, 2011.
Bibtex Entry:
@article{Alvarez2011,
abstract = {BACKGROUND: Existing 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. RESULTS: We 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. CONCLUSIONS: Spgk 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.},
author = {Alvarez, Marco and Qi, Xiaojun and Yan, Changhui},
doi = {10.1186/2041-1480-2-3},
file = {:home/seb/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Alvarez, Qi, Yan - 2011 - A shortest-path graph kernel for estimating gene product semantic similarity.pdf:pdf},
issn = {2041-1480},
journal = {Journal of biomedical semantics},
keywords = {SML-LIB-BIBLIO},
mendeley-tags = {SML-LIB-BIBLIO},
month = jan,
pages = {3},
pmid = {21801410},
title = {{A shortest-path graph kernel for estimating gene product semantic similarity.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3161911\&tool=pmcentrez\&rendertype=abstract},
volume = {2},
year = {2011}
}