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Candidate gene prioritization using graph embedding

Abstract : Candidate genes prioritization allows to rank among a large number of genes, those that are strongly associated with a phenotype or a disease. Due to the important amount of data that needs to be integrate and analyse, gene-to-phenotype association is still a challenging task. In this paper, we evaluated a knowledge graph approach combined with embedding methods to overcome these challenges. We first introduced a dataset of rice genes created from several open-access databases. Then, we used the Translating Embedding model and Convolution Knowledge Base model, to vectorize gene information. Finally, we evaluated the results using link prediction performance and vectors representation using some unsupervised learning techniques.
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Contributor : Pierre Larmande <>
Submitted on : Friday, January 31, 2020 - 4:58:49 AM
Last modification on : Tuesday, February 11, 2020 - 10:10:37 AM
Document(s) archivé(s) le : Friday, May 1, 2020 - 12:57:06 PM


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  • HAL Id : hal-02461886, version 1



Quan Do, Pierre Larmande. Candidate gene prioritization using graph embedding. IEEE-RIVF 2020, Apr 2020, Ho Chi Minh City, Vietnam. ⟨hal-02461886⟩



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