CircRNA and disease incidence relation prediction method based on graph attention mechanism

The invention discloses a circRNA and disease incidence relation prediction method based on a graph attention mechanism, and the method comprises the steps: constructing a prediction model based on the graph attention mechanism for a large number of unknown circRNA-disease incidence relations by using disease ontology data and known circRNA-disease incidenceinformation, and extracting accurate low-dimensional vector representation of circRNA and diseases, and designing a model based on a multilayer neural network to predict an unknown circRNA-disease incidence relation. According to the method, the circRNA related to the disease can be efficiently and reliably predicted through a calculation method, and the human and financial cost of biological verification is saved. 本发明公开了一种基于图注意力机制的circRNA与疾病关联关系预测方法，针对大量未知的circRNA-疾病关联关系，利用疾病本体数据和已知circRNA-疾病关联信息，构建基于图注意力机制的预测模型提取circRNA和疾病的准确的低维向量表示，并设计一种基于多层神经网络的模型预测未知的circRNA-疾病关联关系。本发明通过计算方法可以高效、可靠地预测与疾病相关的circRNA，节省生物验证的人力财力成本。