TY - GEN
T1 - Evaluating topology-based metrics for GO term similarity measures
AU - Jeong, Jong Cheol
AU - Chen, Xue Wen
PY - 2013
Y1 - 2013
N2 - Defining semantic functional similarity measures provides effective means to validate protein function prediction methods and to retrieve biologically relevant information from big biological data. It also improves understanding of interrelationship between genes and gene products (GPs). Currently, one of the most commonly used tools for functionally annotating genes and GPs is the Gene Ontology (GO), which describes genes/GPs using a machine-readable language. To measure the semantic similarity between two GO terms, many studies that are based on GO topology have recently been reported. However, a comprehensive assessment and general guidelines for validating these methods are lacking. In this paper, we collect a large dataset to evaluate five often-used semantic similarity measure methods by estimating sequence similarity, phylogenetic profile similarity, and structural similarity. We further compare the measures in terms of their clustering performance using domains extracted from SCOP database. We describe some key aspects of these measure methods and discuss how the limitations may be addressed as well as some open problems.
AB - Defining semantic functional similarity measures provides effective means to validate protein function prediction methods and to retrieve biologically relevant information from big biological data. It also improves understanding of interrelationship between genes and gene products (GPs). Currently, one of the most commonly used tools for functionally annotating genes and GPs is the Gene Ontology (GO), which describes genes/GPs using a machine-readable language. To measure the semantic similarity between two GO terms, many studies that are based on GO topology have recently been reported. However, a comprehensive assessment and general guidelines for validating these methods are lacking. In this paper, we collect a large dataset to evaluate five often-used semantic similarity measure methods by estimating sequence similarity, phylogenetic profile similarity, and structural similarity. We further compare the measures in terms of their clustering performance using domains extracted from SCOP database. We describe some key aspects of these measure methods and discuss how the limitations may be addressed as well as some open problems.
KW - gene ontology
KW - gene products
KW - semantic functional similarity
UR - http://www.scopus.com/inward/record.url?scp=84894527712&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894527712&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2013.6732457
DO - 10.1109/BIBM.2013.6732457
M3 - Conference contribution
AN - SCOPUS:84894527712
SN - 9781479913091
T3 - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
SP - 43
EP - 48
BT - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Y2 - 18 December 2013 through 21 December 2013
ER -