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DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy
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Outgoing claims: the papers this work cites, builds on, or argues with.
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StackDPP: Stacking-Based Explainable Classifier for Depression Prediction and Finding the Risk Factors among Clinicians
inferred← citeshttps://doi.org/10.3390/genes15091170openalex-snapshot
DTVF: A User-Friendly Tool for Virulence Factor Prediction Based on ProtT5 and Deep Transfer Learning Models
inferred← citeshttps://doi.org/10.3390/ijms222313124openalex-snapshot
UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning
inferred← citeshttps://doi.org/10.3390/ijms231911026openalex-snapshot
DLm6Am: A Deep-Learning-Based Tool for Identifying N6,2′-O-Dimethyladenosine Sites in RNA Sequences
inferred← citeshttps://doi.org/10.3390/microorganisms13071635openalex-snapshot
Generative and Contrastive Self-Supervised Learning for Virulence Factor Identification Based on Protein–Protein Interaction Networks
inferred← citeshttps://doi.org/10.3934/mbe.2022622openalex-snapshot
SPREAD: An ensemble predictor based on DNA autoencoder framework for discriminating promoters in Pseudomonas aeruginosa
inferred← citeshttps://doi.org/10.6084/m9.figshare.24180897openalex-snapshot
Additional file 1 of TIPred: a novel stacked ensemble approach for the accelerated discovery of tyrosinase inhibitory peptides
inferred← citeshttps://doi.org/10.6084/m9.figshare.26607256openalex-snapshot
Additional file 1 of StackTTCA: a stacking ensemble learning-based framework for accurate and high-throughput identification of tumor T cell antigens
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