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

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

Enterprise 2023-12-17 09:18:23 views: null

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

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)

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)

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

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

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

Deep Neural Networks for Time Series Forecasting Based on Stacked Autoencoders

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Detailed explanation of stacked autoencoders (SAE)

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

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

KR-NSSM: Predicting potential miRNA-disease associations based on more reliable negative sample selection (BMC 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)

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