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HSAELDA: Predicting lncRNA-disease associations based on heterogeneous networks and Stacked Autoencoder.

, , , and . BIBM, page 607-612. IEEE, (2022)

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Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification., , , , and . IEEE J. Biomed. Health Informatics, 24 (10): 3002-3011 (2020)Multi-Label Fusion Collaborative Matrix Factorization for Predicting LncRNA-Disease Associations., , , , and . IEEE J. Biomed. Health Informatics, 25 (3): 881-890 (2021)MKL-LP: Predicting Disease-Associated Microbes with Multiple-Similarity Kernel Learning-Based Label Propagation., , , , and . ISBRA, volume 13064 of Lecture Notes in Computer Science, page 3-10. Springer, (2021)A Locality-Constrained Linear Coding-Based Ensemble Learning Framework for Predicting Potentially Disease-Associated MiRNAs., , , , and . ISBRA, volume 13760 of Lecture Notes in Computer Science, page 295-302. Springer, (2022)LncRNA-disease association prediction method based on heterogeneous information completion and convolutional neural network., , , , and . CoRR, (2024)GRPGAT: Predicting CircRNA-disease Associations Based on Graph Random Propagation Network and Graph Attention Network., , , , , and . BIBM, page 233-236. IEEE, (2023)Graph regularized robust non-negative matrix factorization for clustering and selecting differentially expressed genes., , , , , and . BIBM, page 1752-1756. IEEE Computer Society, (2017)Feature selection and clustering via robust graph-laplacian PCA based on capped L1-norm., , , , and . BIBM, page 1741-1745. IEEE Computer Society, (2017)Hypergraph regularized NMF by L2, 1-norm for Clustering and Com-abnormal Expression Genes Selection., , , , and . BIBM, page 578-582. IEEE Computer Society, (2018)Differentially expressed genes selection via Truncated Nuclear Norm Regularization., , , , , and . BIBM, page 1851-1855. IEEE Computer Society, (2016)