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Fig. 4 | BMC Bioinformatics

Fig. 4

From: A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations

Fig. 4

Framework of VGAELDA. Step 1: lncRNA features \(X_l\) are embeddings of lncRNA sequences computed by Word2Vec, while disease features \(X_d\) are associations with genes. Step 2: constructing graph \(G_l\) and \(G_d\) through Eq. (16) for lncRNAs and diseases, respectively. Step 3: GNNql and GNNpl are applied to \(G_l\), that they require \(X_l\) and Y as inputs, while GNNqd and GNNpd applied to \(G_d\) require \(X_d\) and \(Y^T\) as inputs. Step 4: training GNNq and GNNp alternately via variational EM algorithm, while training GNNql and GNNqd collaboratively. Step 5: final result fusion by Eq. (28)

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BMC Bioinformatics

ISSN: 1471-2105

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