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BRWMC: Predicting lncRNA-disease associations based on bi-random walk and matrix completion on disease and lncRNA networks., и . Comput. Biol. Chem., (апреля 2023)LANCMDA: Predicting MiRNA-Disease Associations via LightGBM with Attributed Network Construction., , , , , и . ICIC (3), том 14088 из Lecture Notes in Computer Science, стр. 291-299. Springer, (2023)Robust Graph Regularized Extreme Learning Machine Auto Encoder and Its Application to Single-Cell Samples Classification., , , , и . ICIC (2), том 12464 из Lecture Notes in Computer Science, стр. 537-545. Springer, (2020)A Simple Review of Sparse Principal Components Analysis., , , , , и . ICIC (2), том 9772 из Lecture Notes in Computer Science, стр. 374-383. Springer, (2016)Performance Analysis of Non-negative Matrix Factorization Methods on TCGA Data., , , , , и . ICIC (2), том 10955 из Lecture Notes in Computer Science, стр. 407-418. Springer, (2018)L2, 1-Extreme Learning Machine: An Efficient Robust Classifier for Tumor Classification., , , , и . Comput. Biol. Chem., (2020)Supervised Discriminative Sparse PCA for Com-Characteristic Gene Selection and Tumor Classification on Multiview Biological Data., , , , и . CoRR, (2019)SLGCN: Structure-enhanced line graph convolutional network for predicting drug-disease associations., , , , и . Knowl. Based Syst., (января 2024)Multi-modal imaging genetics data fusion by deep auto-encoder and self-representation network for Alzheimer's disease diagnosis and biomarkers extraction., , , , и . Eng. Appl. Artif. Intell., (2024)Regularized Non-Negative Matrix Factorization for Identifying Differentially Expressed Genes and Clustering Samples: A Survey., , , , , и . IEEE ACM Trans. Comput. Biol. Bioinform., 15 (3): 974-987 (2018)