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March 7, 2024BMC BioinformaticsOpen Access

Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model

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Authors

SAShahid AkbarARAli RazaQZQuan Zou

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Akbar et al. (2024) studied this question.

synapsesocial.com/papers/68e7541bb6db6435876cbd54https://doi.org/10.1186/s12859-024-05726-5
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation2019 · 136 citations
  2. 2AI4AVP: an antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation2022 · 80 citations
  3. 3AVPIden: a new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches2021 · 93 citations
  4. 4Part 1: Simple Definition and Calculation of Accuracy, Sensitivity and Specificity.2015 · 465 citations
  5. 5AFP-SPTS: An Accurate Prediction of Antifreeze Proteins Using Sequential and Pseudo-Tri-Slicing Evolutionary Features with an Extremely Randomized Tree2023 · 41 citations