Constructing a gene semantic similarity network for the inference of disease genes

Abstract

MotivationThe inference of genes that are truly associated with inherited human diseases from a set of candidates resulting from genetic linkage studies has been one of the most challenging tasks in human genetics. Although several computational approaches have been proposed to prioritize candidate genes relying on protein-protein interaction (PPI) networks, these methods can usually cover less than half of known human genes.ResultsWe propose to rely on the biological process domain of the gene ontology to construct a gene semantic similarity network and then use the network to infer disease genes. We show that the constructed network covers about 50% more genes than a typical PPI network. By analyzing the gene semantic similarity network with the PPI network, we show that gene pairs tend to have higher semantic similarity scores if the corresponding proteins are closer to each other in the PPI network. By analyzing the gene semantic similarity network with a phenotype similarity network, we show that semantic similarity scores of genes associated with similar diseases are significantly different from those of genes selected at random, and that genes with higher semantic similarity scores tend to be associated with diseases with higher phenotype similarity scores. We further use the gene semantic similarity network with a random walk with restart model to infer disease genes. Through a series of large-scale leave-one-out cross-validation experiments, we show that the gene semantic similarity network can achieve not only higher coverage but also higher accuracy than the PPI network in the inference of disease genes.Contactruijiang@tsinghua.edu.cn

Ontology-based disease similarity network for disease gene prediction

“…Therefore, three heterogeneous networks were constructed for our method, i.e., HPONetPPINet, HPONet-GENet and HPONet-GONet. Meanwhile, heterogeneous networks in [32,34] were OMIMNet-GONet and OMIMNet-PPINet, respectively. In addition to these five heterogeneous networks, we constructed OMIMNet-GENet for the comparison.…”

“…First, following the same procedure as in [32,34], we collected a phenotypic disease similarity matrix from [50], where an element of the matrix represents degree of similarity between two phenotypes. The similarities in this matrix were calculated based on various text mining algorithms on OMIM records, which describe diseases using natural language [33].…”

“…More importantly, the RWRH algorithm can be extended to use any network of genes/proteins as well as disease similarity one. Indeed, a recent RWRH-based method has used a semantic similarity network of genes instead of the protein interaction network [34] and shown to outperform the original one [32]. We also note that a disease similarity network can be constructed based on shared disease gene [30], shared pathways [35], shared miRNA [36], shared protein complex [37], shared disease ontology [38] and disease comorbidity [39].…”

“…Then, this network was integrated with a gene/protein network by known disease phenotype-gene associations. We compared our method with the one relied on the OMIMbased disease similarity network as in [32,34]. In which, the gene/protein network can be the protein interaction network as in [32], the gene semantic similarity network as in [34] as well as one constructed based on expression profiles of genes.…”

“…We compared our method with the one relied on the OMIMbased disease similarity network as in [32,34]. In which, the gene/protein network can be the protein interaction network as in [32], the gene semantic similarity network as in [34] as well as one constructed based on expression profiles of genes. Experimental results show that the performance of our method is better than that based on the OMIM-based disease similarity network irrespective of the gene/protein networks.…”