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 phenotypiceffect of a deleted or mutated gene, which is one part of gene annotation, has caughtbroad attention. However, there have been few measures to study phenotypicsimilarity with the data from Human Phenotype Ontology (HPO) database, thereforemore analogous measures should be developed and investigated. We used fivesemantic similarity-based measures (Jiang and Conrath’s, Lin’s, Schlicker’s, Yu’s andWu’s) to calculate the human phenotypic similarity between genes (PSG) with datafrom HPO database. In addition, the paper also proposed the novel method calculatingPSG by combination of Cosine and the Level of gene annotation in HPO (CosL), andevaluated their accuracy with information of protein-protein interaction, proteincomplex, protein family, gene function or DNA sequence. Compared with the genepairs that were random selected, the results of these methods were statisticallysignificant (P<0.001). Furthermore, we assessed the performance of these sixmeasures by receiver operating characteristic (ROC) curve analysis, and found thatmost of them performed better than the previous methods. This work had proved thatthese measures based on semantic similarity for calculation of PSG were effective forhierarchical structure data. Our study contributes to the development and optimizationof novel algorithms of PSG calculation and provides more alternative methods toresearchers as well as tools and directions for PSG study.