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Fig. 7 | BMC Bioinformatics

Fig. 7

From: A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs

Fig. 7

The flowchart of LGDLDA. (1) LGDLDA uses multiple association similarity matrices to build lncRNA-gene-disease association network. (2) Based on the matrices generated in the first step, LGDLDA uses the association similarity matrices combined with neural network to calculate the neighborhood information of lncRNAs and diseases, and further embeds it into the low-dimensional spatial node representations. (3) LGDLDA uses embedded representations to generate the reconstructed matrix to approximate the original matrix, and learns as much information in the original matrix as possible in the optimization of the loss function. (4) LGDLDA sorts the elements in the learned association matrix and selects the top values to predict cancer-related lncRNAs

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