Evaluation of GO-based functional similarity measures using S. cerevisiae protein interaction and expression profile data

Abstract

BackgroundResearchers interested in analysing the expression patterns of functionally related genes usually hope to improve the accuracy of their results beyond the boundaries of currently available experimental data. Gene ontology (GO) data provides a novel way to measure the functional relationship between gene products. Many approaches have been reported for calculating the similarities between two GO terms, known as semantic similarities. However, biologists are more interested in the relationship between gene products than in the scores linking the GO terms. To highlight the relationships among genes, recent studies have focused on functional similarities.ResultsIn this study, we evaluated five functional similarity methods using both protein-protein interaction (PPI) and expression data of S. cerevisiae. The receiver operating characteristics (ROC) and correlation coefficient analysis of these methods showed that the maximum method outperformed the other methods. Statistical comparison of multiple- and single-term annotated proteins in biological process ontology indicated that genes with multiple GO terms may be more reliable for separating true positives from noise.ConclusionThis study demonstrated the reliability of current approaches that elevate the similarity of GO terms to the similarity of proteins. Suggestions for further improvements in functional similarity analysis are also provided.

Characterisation of semantic similarity on gene ontology based on a shortest path approach

“…The evaluation of these methods shows that the similarity obtained using the 'Max' method is best correlated with the gene expression data. However, the 'Max' method is more sensitive to outliers, while the 'Ave' method is relatively stable (Xu et al, 2008).…”

“…In our experiments, we used the annotations from BP ontology. According to (Xu et al, 2008), terms at the top levels will create noise. Therefore, in our experiments, annotations at the first three levels were removed.…”

“…To compute the semantic similarity over gene products, we used the 'Max' operation for all the five methods, since it consistently results in the best correlation scores for all the methods measuring the semantic similarity over the terms (Xu et al, 2008). …”

“…To evaluate the accuracy of the existing methods for measuring semantic similarity, manually curated information and experimental data (e.g., Protein-Protein Interaction (PPI) (Xu et al, 2008), pathway information (Wang et al, 2007), and gene expression data (Xu et al, 2008)) have been used. In the previous work, researchers often use the Pearson correlation score between the similarity based on gene expression data and the computed semantic similarity to assess the accuracy of the proposed methods.…”

Domain‐mediated protein interaction prediction: From genome to network

“…Guo et al compared a number of network-based and information content-based semantic similarity methods in distinguishing true and false human PPIs, and concluded that the average (AVG) method by Resnik performed best in AUROC analysis [55][56][57][58]. Xu et al compared the AVG and maximum (MAX) methods by Resnik to a number of semantic similarity methods specifically developed for GO, and concluded that the MAX method by Resnik outperforms others when considering the three ontologies of GO either individually or together [55,[59][60][61][62]. More recently, our group developed the Topological Clustering Semantic Similarity (TCSS) method, which uses a novel normalization technique before computing similarity [63].…”