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Increasing the reliability of protein-protein interaction networks via non-convex semantic embedding.

, , and . Neurocomputing, (2013)

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Predicting Protein-Protein Interactions from Amino Acid Sequences Using SaE-ELM Combined with Continuous Wavelet Descriptor and PseAA Composition., , , , and . ICIC (2), volume 9226 of Lecture Notes in Computer Science, page 634-645. Springer, (2015)A Gaussian Kernel Similarity-Based Linear Optimization Model for Predicting miRNA-lncRNA Interactions., , , , and . ICIC (2), volume 12464 of Lecture Notes in Computer Science, page 316-325. Springer, (2020)Prediction of lncRNA-miRNA Interactions via an Embedding Learning Graph Factorize Over Heterogeneous Information Network., , , , and . ICIC (2), volume 12464 of Lecture Notes in Computer Science, page 270-278. Springer, (2020)Efficient framework for predicting MiRNA-disease associations based on improved hybrid collaborative filtering., , , , and . BMC Medical Informatics Decis. Mak., 21-S (1): 254 (2021)NSECDA: Natural Semantic Enhancement for CircRNA-Disease Association Prediction., , , , , and . IEEE J. Biomed. Health Informatics, 26 (10): 5075-5084 (2022)MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules., , , , , , , and . BMC Bioinform., 25 (1): 6 (December 2024)BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path., , , , and . J. Chem. Inf. Model., 63 (16): 5384-5394 (August 2023)Incorporation of Efficient Second-Order Solvers Into Latent Factor Models for Accurate Prediction of Missing QoS Data., , , , , , and . IEEE Trans. Cybern., 48 (4): 1216-1228 (2018)iGRLCDA: identifying circRNA-disease association based on graph representation learning., , , , , , and . Briefings Bioinform., (2022)GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions., , , , , , , and . Briefings Bioinform., (2022)