Abstract
Biological ontologies, such as the Human Phenotype Ontology (HPO) and the Gene Ontology (GO), are extensively used in biomedical research to investigate the complex relationship that exists between the phenome and the genome. The interpretation of the encoded information requires methods that efficiently interoperate between multiple ontologies providing molecular details of disease-related features. To this aim, we present GenOtype PHenotype ExplOrer (GOPHER), a framework to infer associations between HPO and GO terms harnessing machine learning and large-scale parallelism and scalability in High-Performance Computing. The method enables to map genotypic features to phenotypic features thus providing a valid tool for bridging functional and pathological annotations. GOPHER can improve the interpretation of molecular processes involved in pathological conditions, displaying a vast range of applications in biomedicine.
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Notes
- 1.
Vectors are parallel-friendly structures that allow to easily split the elements among different compute elements.
- 2.
Path frequencies for connected pairs are computed without considering the edges that directly connect the phenotype and genotype to the same gene, to avoid biasing the model towards already existing pairs.
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Acknowledgements
This work has been developed with the support of the Severo Ochoa Program (SEV-2015-0493); the Spanish Ministry of Science and Innovation (TIN2015-65316-P); and the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement.
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Josep-Fabregó, M. et al. (2020). GOPHER, an HPC Framework for Large Scale Graph Exploration and Inference. In: Jagode, H., Anzt, H., Juckeland, G., Ltaief, H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science(), vol 12321. Springer, Cham. https://doi.org/10.1007/978-3-030-59851-8_13
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