Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes
KAUST DepartmentBio-Ontology Research Group (BORG)
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-09-08
Print Publication Date2018-09-01
Permanent link to this recordhttp://hdl.handle.net/10754/627767
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AbstractMotivation In the past years, several methods have been developed to incorporate information about phenotypes into computational disease gene prioritization methods. These methods commonly compute the similarity between a disease's (or patient's) phenotypes and a database of gene-to-phenotype associations to find the phenotypically most similar match. A key limitation of these methods is their reliance on knowledge about phenotypes associated with particular genes which is highly incomplete in humans as well as in many model organisms such as the mouse. Results We developed SmuDGE, a method that uses feature learning to generate vector-based representations of phenotypes associated with an entity. SmuDGE can be used as a trainable semantic similarity measure to compare two sets of phenotypes (such as between a disease and gene, or a disease and patient). More importantly, SmuDGE can generate phenotype representations for entities that are only indirectly associated with phenotypes through an interaction network; for this purpose, SmuDGE exploits background knowledge in interaction networks comprised of multiple types of interactions. We demonstrate that SmuDGE can match or outperform semantic similarity in phenotype-based disease gene prioritization, and furthermore significantly extends the coverage of phenotype-based methods to all genes in a connected interaction network. Availability and implementation https://github.com/bio-ontology-research-group/SmuDGE.
CitationAlshahrani M, Hoehndorf R (2018) Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes. Bioinformatics 34: i901–i907. Available: http://dx.doi.org/10.1093/bioinformatics/bty559.
SponsorsThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and FCC/1/1976-08-01.
PublisherCold Spring Harbor Laboratory
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