SPPPred: Sequence-based Protein-Peptide binding residue Prediction using genetic programming and ensemble learning

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@Article{Shafiee:TCBB,

• author = "Shima Shafiee and Abdolhossein Fathi and Ghazaleh Taherzadeh",
• title = "{SPPPred:} Sequence-based Protein-Peptide binding residue Prediction using genetic programming and ensemble learning",
• journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
• year = "2023",
• volume = "20",
• number = "3",
• pages = "2029--2040",
• month = may # "-" # jun,
• keywords = "genetic algorithms, genetic programming, Binding residue prediction, ensemble learning, protein-peptide interaction, sequence-based",
• ISSN = "1557-9964",
• DOI = "doi:10.1109/TCBB.2022.3230540",
• code_url = "https://github.com/GTaherzadeh/SPPPred.git",
• size = "12 pages",
• abstract = "Peptide-binding proteins play significant roles in various applications such as gene expression, metabolism, signal transmission, DNA (Deoxyribose Nucleic Acid) repair, and replication. Investigating the binding residues in protein-peptide complexes, especially from their sequence only, is challenging experimentally and computationally. Although several computational approaches have been introduced to determine and predict these binding residues, there is still ample room to improve the prediction performance. we introduce a novel ensemble machine learning-based approach called SPPPred (Sequence-based Protein-Peptide binding residue Prediction) to predict protein-peptide binding residues. First, we extract relevant sequential information and employ genetic programming algorithm for feature construction to find more distinctive features. We then, in the next step, build an ensemble-based machine learning classifier to predict binding residues. The proposed method shows consistent and comparable performance on both ten-fold cross-validation and independent test set. Furthermore, SPPPred yields F-Measure (F-M), Accuracy(ACC), and Matthews' Correlation Coefficient (MCC) of 0.310, 0.949, and 0.230 on the independent test set, respectively, which outperforms other competing methods by approximately up to 9 percent on the independent test set. SPPPred is publicly available. https://github.com/GTaherzadeh/SPPPred.git",
• notes = "Also known as \cite{9992060}",

}

Genetic Programming entries for Shima Shafiee Abdolhossein Fathi Ghazaleh Taherzadeh

Citations