Calculating phenotypic similarity between genes using hierarchical structure data based on semantic similarity.
Phenotypic similarity is correlated with a number of measures of gene function, such as relatedness at the level of direct protein-protein interaction. The phenotypic effect of a deleted or mutated gene, which is one part of gene annotation, has caught broad attention. However, there have been few measures to study phenotypic similarity with the data from Human Phenotype Ontology (HPO) database, therefore more analogous measures should be developed and investigated. We used five semantic similarity-based measures (Jiang and Conrath, Lin, Schlicker, Yu and Wu) to calculate the human phenotypic similarity between genes (PSG) with data from HPO database, and evaluated their accuracy with information of protein-protein interaction, protein complex, protein family, gene function or DNA sequence. Compared with the gene pairs that were random selected, the results of these methods were statistically significant (all P<0.001). Furthermore, we assessed the performance of these five measures by receiver operating characteristic (ROC) curve analysis, and found that most of them performed better than the previous methods. This work had proved that these measures based on semantic similarity for calculation of PSG were effective for hierarchical structure data. Our study contributes to the development and optimization of novel algorithms of PSG calculation and provides more alternative methods to researchers as well as tools and directions for PSG study.
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, PR China.
Shanzhen
S
Zhang
Zhiqiang
Z
Chang
Zhenqi
Z
Li
Huizi
H
DuanMu
Zihui
Z
Li
Kening
K
Li
Yufeng
Y
Liu
Fujun
F
Qiu
Yan
Y
Xu
D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D006801:Humans; D010641:Phenotype; D012372:ROC Curve; D012660:Semantics