Candidate gene prioritization using graph embedding

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.

GenRank: an R/Bioconductor package for prioritization of candidate genes

Summary:Modern high-throughput studies often yield long lists of genes, of which a fraction are of high relevance to the phenotype of interest. To prioritize the candidate genes of complex genetic traits, our R/Bioconductor package GenRank provides methods that are based on convergent evidence obtained from multiple independent evidence layers. The package facilitates an extensible framework that allows a further addition of novel methods for candidate gene prioritization.Availability and Implementation:The methods are implemented in R and available as a package in Bioconductor repository (http://bioconductor.org/packages/GenRank/).Contact:[email protected]