CircRNA-disease prediction based on graph neural networks and random forests
Circular RNA(CircRNA)are widely involved in human disease processes,and their mutations and dysregulation are closely associated with many human diseases.Therefore,establishing an efficient and accurate prediction algorithm between CircRNA and diseases is important for making prevention of disease occurrence in advance as well as treatment programs after the onset of diseases.A new algorithm based on graph neural network and random forest is proposed to predict CircRNA-disease association algorithm,in the hierarchical network representation embedding part by constructing a heterogeneous network,according to the proximity of the network graph,the nodes and edges of the network graph are layered,and the nodes and edges in the original graph are merged recursively to obtain a number of smaller sub-networks with similar characteristics,and the size of the sub-networks decreases with deeper layering until the smallest sub-network is obtained.The size of the sub-networks decreases with the depth of layering until the smallest sub-network is obtained,which is preprocessed using the node2vec network graph wandering algorithm,and then the feature vectors of all the nodes are inputted into the random forest classifier to identify potential CircRNA-disease associations and thus make predictions.
CircRNA-disease association predictiongraph neural networknode2vecrandom forest
万方数据
维普
