DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction

Deep Learning Approaches for lncRNA-Mediated Mechanisms: A Comprehensive Review of Recent Developments

“…From the assortment of categories that have emerged, one of the most extensively researched topics is the lncRNA–disease association. Multiple studies have endeavored to harness the predictive capabilities of various deep learning architectures to ascertain the relationship between lncRNAs and diseases [ 29 , 34 , 48 ]. The prodigious quantity of investigations in this domain highlights the compelling implications these associations could potentially have on clinical diagnostics and therapeutics.…”

“…Prediction of lncRNA-disease associations ACLDA, combining autoencoders, CNN, and attention mechanism [27]; CapsNet-LDA, predicting lncRNA-disease associations using capsule network and attention [28]; DBNLDA, deep belief network-based lncRNA-disease association prediction [29]; Deep learning cluster analysis of lncRNAs in heart failure [30]; DeepMNE, deep multi-network embedding for lncRNA-disease prediction [31]; DHNLDA, deep hierarchical network with stacked autoencoder and ResNet [32]; DMFLDA, deep matrix factorization for predicting lncRNA-disease associations [33]; Dual attention network, enhances the learning of lncRNA-disease feature sets [34]; GCRFLDA, graph convolutional matrix completion-based lncRNA-disease prediction [35]; gGATLDA, lncRNA-disease associations prediction via graph-level attention networks [36]; GSMV, learning of global dependencies and multi-semantics within heterogeneous graphs [37]; GTAN, graph neural network for predicting lncRNA-disease associations [38]; HGATLDA, heterogeneous graph attention network for lncRNA-disease associations [39]; HGNNLDA, heterogeneous graph neural network for lncRNA-disease association [40]; Identifying cancer transcriptome signatures via deep learning interpretation [41]; iLncRNAdis-FB, CNN with fusing biological feature blocks [42]; LDACE, combining extreme learning machine with CNN [43]; LDICDL, identifying lncRNA-disease associations using collaborative deep learning [44]; LGDLDA, predicting disease-related lncRNAs via multiomics data and machine learning [45]; LR-GNN, graph neural network-based prediction of molecular associations [46]; MAGCNSE, lncRNA-disease association prediction via multi-view graph convolutional network [47]; MCA-Net, predicting lncRNA-disease associations using attention CNN [48]; MLMKDNN, predicting ncRNA-disease associations via deep multiple kernel learning [49]; MLGCNET, predicting lncRNA-disease associations using multi-layer graph embedding [50]; Multi-run concrete autoencoder identifying prognostic lncRNAs for cancers [51]; NELDA, predicting lncRNA-disease associations via deep autoencoder models [52]; Novel computational approach, lncRNA-disease prediction via B...…”