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 https://github.com/bio-ontology-research-group/DL2Vec.
In recent years, several genes have been implicated in the variable disease presentation of global developmental delay (GDD) and intellectual disability (ID). The endoplasmic reticulum membrane protein complex (EMC) family is known to be involved in GDD and ID. Homozygous variants of EMC1 are associated with GDD, scoliosis, and cerebellar atrophy, indicating the relevance of this pathway for neurogenetic disorders. EMC10 is a bone marrow-derived angiogenic growth factor that plays an important role in infarct vascularization and promoting tissue repair. However, this gene has not been previously associated with human disease. Herein, we describe a Saudi family with two individuals segregating a recessive neurodevelopmental disorder. Both of the affected individuals showed mild ID, speech delay, and GDD. Wholeexome sequencing (WES) and Sanger sequencing were performed to identify candidate genes. Further, to elucidate the functional effects of the variant, quantitative real-time PCR (RT-qPCR)-based expression analysis was performed. WES revealed a homozygous splice acceptor site variant (c.679-1G>A) in EMC10 (chromosome 19q13.33) that segregated perfectly within the family. RT-qPCR
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 https://github. com/bio-ontology-research-group/DL2Vec.
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