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A feature extraction method based on noise reduction for circRNA-miRNA interaction prediction combining multi-structure features in the association networks.

, , , , , , , and . Briefings Bioinform., (May 2023)

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Prediction of protein-protein interactions from amino acid sequences using extreme learning machine combined with auto covariance descriptor., , , , and . Memetic Computing, page 80-85. IEEE, (2013)Multi-level Subgraph Representation Learning for Drug-Disease Association Prediction Over Heterogeneous Biological Information Network., , , , , , and . ICIC (3), volume 14088 of Lecture Notes in Computer Science, page 156-167. Springer, (2023)Fusing Higher and Lower-Order Biological Information for Drug Repositioning via Graph Representation Learning., , , , , , , and . IEEE Trans. Emerg. Top. Comput., 12 (1): 163-176 (January 2024)Distributed Winner-Take-All in Dynamic Networks., , , and . IEEE Trans. Autom. Control., 62 (2): 577-589 (2017)GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association., , , , and . IEEE J. Biomed. Health Informatics, 28 (3): 1742-1751 (March 2024)iCDA-CGR: Identification of circRNA-disease associations based on Chaos Game Representation., , , , , and . PLoS Comput. Biol., (2020)In silico drug repositioning using deep learning and comprehensive similarity measures., , , , , and . BMC Bioinform., 22-S (3): 293 (2021)Inferring Disease-Associated Piwi-Interacting RNAs via Graph Attention Networks., , , , and . ICIC (2), volume 12464 of Lecture Notes in Computer Science, page 239-250. Springer, (2020)WGMFDDA: A Novel Weighted-Based Graph Regularized Matrix Factorization for Predicting Drug-Disease Associations., , , , and . ICIC (3), volume 12465 of Lecture Notes in Computer Science, page 542-551. Springer, (2020)Precise Prediction of Pathogenic Microorganisms Using 16S rRNA Gene Sequences., , , , , , and . ICIC (2), volume 11644 of Lecture Notes in Computer Science, page 138-150. Springer, (2019)