MMGCN: Application of multi-view multi-channel attention map convolutional network in prediction of association between miRNA and disease (Briefings in Bioinformatics) - Code World

MMGCN: Application of multi-view multi-channel attention map convolutional network in prediction of association between miRNA and disease (Briefings in Bioinformatics)

Enterprise 2023-12-17 09:21:08 views: null

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MMGCN: Application of multi-view multi-channel attention map convolutional network in prediction of association between miRNA and disease (Briefings in Bioinformatics)

MKGAT: miRNA-disease association prediction based on graph attention network and double Laplacian regularized least squares (Briefings in Bioinformatics)

GMDA: Combining pairwise neighborhood topology and miRNA family and clustering attributes for miRNA-disease association prediction (Briefings in Bioinformatics)

ERMDA: An ensemble learning framework based on resampling to predict the association between miRNA and disease (Briefings in Bioinformatics)

VGAMF: miRNA-disease association prediction based on multi-view variational graph autoencoders and matrix factorization (IEEE Journal of Biomedical and Health Informatics)

SAEMDA: Prediction of potential miRNA-disease correlations based on stacked autoencoders (Briefings in Bioinformatics)

HGANMDA: Hierarchical Graph Attention Network (Molecular Therapy) for miRNA and Disease Association Prediction

MDA-CNN: Learning-based neural network miRNA-disease association identification framework (Bioinformatics)

GMDA: Combining pairwise neighborhood topology and miRNA family and clustering attributes for miRNA-disease association prediction (Briefings in Bioinformatics)

GMDA: Combining pairwise neighborhood topology and miRNA family and clustering attributes for miRNA-disease association prediction (Briefings in Bioinformatics)

GMDA: Combining pairwise neighborhood topology and miRNA family and clustering attributes for miRNA-disease association prediction (Briefings in Bioinformatics)

Multi-scale convolutional network traffic prediction (MSCN) based on spatiotemporal attention

SGAEMDA: miRNA-disease association prediction (cells) based on stacked graph autoencoders

DAEMKL: Multi-kernel learning-based deep autoencoder predicts miRNA-disease association (IEEE Transactions on Neural Networks and Learning Systems)

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