Article

Candidate gene prioritization using graph embedding

2020-02-03

Abstract excerpt

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...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: e3a6fc37-d3e2-55f1-8bf7-43deb746d69e
DOI: 10.1101/2020.02.03.927913

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Candidate gene prioritization using graph embeddingDOI 10.1101/2020.02.03.927913Read

New Gene Embedding Learned from Biomedical Literature and Its Application in Identifying Cancer DriversSemantic proximity Read

A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.Semantic proximity Read

XG-PUL: A Network-Based Framework for Disease Gene Prioritization Using Graph Representation Learning and Positive-Unlabeled LearningSemantic proximity Read

PGCN: Disease gene prioritization by disease and gene embedding through graph convolutional neural networksSemantic proximity Read

PREDICTING GENE DISEASE ASSOCIATIONS WITH KNOWLEDGE GRAPH EMBEDDINGS FOR DISEASES WITH CURTAILED INFORMATIONSemantic proximity Read

Unsupervised co-optimization of a graph neural network and a knowledge graph embedding model to prioritize causal genes for Alzheimer’s DiseaseSemantic proximity Read

RGPA-GCN: Graph convolutional networks for rice gene-phenotype association prediction.Semantic proximity Read

An inductive, supervised approach for predicting gene–disease associations using phenotype ontologiesSemantic proximity Read

Discovering implicit associations among critical biological entities.Semantic proximity Read

Predicting candidate genes from phenotypes, functions, and anatomical site of expressionSemantic proximity Read

Identifying genes targeted by disease-associated non-coding SNPs with a protein knowledge graph.Semantic proximity Read

Predicting candidate genes from phenotypes, functions and anatomical site of expression.Semantic proximity Read