An improved method for functional similarity analysis of genes based on Gene Ontology.
BACKGROUND
Measures of gene functional similarity are essential tools for gene clustering, gene function prediction, evaluation of protein-protein interaction, disease gene prioritization and other applications. In recent years, many gene functional similarity methods have been proposed based on the semantic similarity of GO terms. However, these leading approaches may make errorprone judgments especially when they measure the specificity of GO terms as well as the IC of a term set. Therefore, how to estimate the gene functional similarity reliably is still a challenging problem.


RESULTS
We propose WIS, an effective method to measure the gene functional similarity. First of all, WIS computes the IC of a term by employing its depth, the number of its ancestors as well as the topology of its descendants in the GO graph. Secondly, WIS calculates the IC of a term set by means of considering the weighted inherited semantics of terms. Finally, WIS estimates the gene functional similarity based on the IC overlap ratio of term sets. WIS is superior to some other representative measures on the experiments of functional classification of genes in a biological pathway, collaborative evaluation of GO-based semantic similarity measures, protein-protein interaction prediction and correlation with gene expression. Further analysis suggests that WIS takes fully into account the specificity of terms and the weighted inherited semantics of terms between GO terms.


CONCLUSIONS
The proposed WIS method is an effective and reliable way to compare gene function. The web service of WIS is freely available at http://nclab.hit.edu.cn/WIS/ .
Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Zhen
Z
Tian
Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Chunyu
C
Wang
Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China. maozuguo@hit.edu.cn.
Maozu
M
Guo
Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.
Xiaoyan
X
Liu
Department of computer Science and Engineering, Harbin Institute of Technology, Harbin, 150001, People's Republic of China.|Department of Information Management and Information System, Northeast Forestry University, Harbin, 150001, People's Republic of China.
Zhixia
Z
Teng
D019295:Computational Biology / Q000379:methods*; D005656:Fungal Proteins / Q000235:genetics / Q000378:metabolism; D063990:Gene Ontology; D006801:Humans; D025941:Protein Interaction Mapping
D005656:Fungal Proteins
Gene Ontology; Gene functional similarity; Specificity of terms; Weighted inherited semantics