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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)

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CGMDA: An Approach to Predict and Validate MicroRNA-Disease Associations by Utilizing Chaos Game Representation and LightGBM., , and . IEEE Access, (2019)An efficient circRNA-miRNA interaction prediction model by combining biological text mining and wavelet diffusion-based sparse network structure embedding., , , , , and . Comput. Biol. Medicine, (October 2023)GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm., , , , and . PLoS Comput. Biol., (2020)Knowledge graph embedding for profiling the interaction between transcription factors and their target genes., , , , , , , and . PLoS Comput. Biol., (2023)Prediction of protein self-interactions using stacked long short-term memory from protein sequences information., , , , , and . BMC Syst. Biol., 12 (8): 129 (2018)Prediction of protein-protein interactions from amino acid sequences with ensemble extreme learning machines and principal component analysis., , , , and . BMC Bioinform., 14 (S-8): S10 (2013)GANCDA: a novel method for predicting circRNA-disease associations based on deep generative adversarial network., , , , and . Int. J. Data Min. Bioinform., 23 (3): 265-283 (2020)Prediction of lncRNA-Disease Associations from Heterogeneous Information Network Based on DeepWalk Embedding Model., , , , , , , and . ICIC (3), volume 12465 of Lecture Notes in Computer Science, page 291-300. Springer, (2020)MGRCDA: Metagraph Recommendation Method for Predicting CircRNA-Disease Association., , , and . IEEE Trans. Cybern., 53 (1): 67-75 (2023)Highly Efficient Framework for Predicting Interactions Between Proteins., , , and . IEEE Trans. Cybern., 47 (3): 731-743 (2017)