Semantic Disease Gene Embeddings (SmuDGE): phenotype-based disease gene prioritization without phenotypes
Open Access
- 1 September 2018
- journal article
- research article
- Published by Oxford University Press (OUP) in Bioinformatics
- Vol. 34 (17), i901-i907
- https://doi.org/10.1093/bioinformatics/bty559
Abstract
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. 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. https://github.com/bio-ontology-research-group/SmuDGEFunding Information
- King Abdullah University of Science and Technology
- KAUST
- Office of Sponsored Research
- OSR (URF/1/3454-01-01, FCC/1/1976-08-01)
This publication has 32 references indexed in Scilit:
- “Guilt by Association” Is the Exception Rather Than the Rule in Gene NetworksPLoS Computational Biology, 2012
- MouseFinder: Candidate disease genes from mouse phenotype dataHuman Mutation, 2012
- PhenomeNET: a whole-phenome approach to disease gene discoveryNucleic Acids Research, 2011
- Prioritizing candidate disease genes by network-based boosting of genome-wide association dataGenome Research, 2011
- The STRING database in 2011: functional interaction networks of proteins, globally integrated and scoredNucleic Acids Research, 2010
- Clinical Diagnostics in Human Genetics with Semantic Similarity Searches in OntologiesAmerican Journal of Human Genetics, 2009
- Metrics for GO based protein semantic similarity: a systematic evaluationBMC Bioinformatics, 2008
- Walking the Interactome for Prioritization of Candidate Disease GenesAmerican Journal of Human Genetics, 2008
- FunSimMat: a comprehensive functional similarity databaseNucleic Acids Research, 2007
- Gene prioritization through genomic data fusionNature Biotechnology, 2006