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Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools., , , , и . Briefings Bioinform., 21 (2): 408-420 (2020)Multi-CGAN: Deep Generative Model-Based Multiproperty Antimicrobial Peptide Design., , , и . J. Chem. Inf. Model., 64 (1): 316-326 (января 2024)ToxIBTL: prediction of peptide toxicity based on information bottleneck and transfer learning., , , , и . Bioinform., 38 (6): 1514-1524 (2022)StructuralDPPIV: a novel deep learning model based on atom structure for predicting dipeptidyl peptidase-IV inhibitory peptides., , , , , , , и . Bioinform., (февраля 2024)PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning., , , и . Bioinform., 35 (21): 4272-4280 (2019)ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides., , , , и . Briefings Bioinform., 21 (5): 1846-1855 (2020)PSSP-MVIRT: peptide secondary structure prediction based on a multi-view deep learning architecture., , , , , , , , , и . Briefings Bioinform., (2021)AFP-MFL: accurate identification of antifungal peptides using multi-view feature learning., , , , , , и . Briefings Bioinform., (января 2023)MPI-VGAE: protein-metabolite enzymatic reaction link learning by variational graph autoencoders., , , , , , , , , и . Briefings Bioinform., (июля 2023)Distant metastasis identification based on optimized graph representation of gene interaction patterns., , , и . Briefings Bioinform., (2022)