Predicting Candidate Genes From Phenotypes, Functions, And Anatomical Site Of Expression.

Jun Chen, Azza Th. Althagafi, Robert Hoehndorf

Research output: Contribution to journalArticlepeer-review

23 Scopus citations


MOTIVATION:Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine learning models. RESULTS:We developed a novel graph-based machine learning method for biomedical ontologies which is able to exploit axioms in ontologies and other graph-structured data. Using our machine learning method, we embed genes based on their associated phenotypes, functions of the gene products, and anatomical location of gene expression. We then develop a machine learning model to predict gene-disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state of the art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes which are associated with phenotypes, functions, or site of expression. AVAILABILITY:Software and data are available at
Original languageEnglish (US)
JournalBioinformatics (Oxford, England)
StatePublished - Oct 14 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-19
Acknowledged KAUST grant number(s): FCC/1/1976-08-01, URF/1/3454-01-01, URF/1/3790-01-01
Acknowledgements: This 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, URF/1/3790-01-01, FCC/1/1976-08-01, and FCC/1/1976-08-08.


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