SPPPred: Sequence-based Protein-Peptide binding residue Prediction using genetic programming and ensemble learning
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gp-bibliography.bib Revision:1.8010
- @Article{Shafiee:TCBB,
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author = "Shima Shafiee and Abdolhossein Fathi and
Ghazaleh Taherzadeh",
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title = "{SPPPred:} Sequence-based Protein-Peptide binding
residue Prediction using genetic programming and
ensemble learning",
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journal = "IEEE/ACM Transactions on Computational Biology and
Bioinformatics",
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year = "2023",
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volume = "20",
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number = "3",
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pages = "2029--2040",
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month = may # "-" # jun,
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keywords = "genetic algorithms, genetic programming, Binding
residue prediction, ensemble learning, protein-peptide
interaction, sequence-based",
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ISSN = "1557-9964",
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DOI = "doi:10.1109/TCBB.2022.3230540",
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code_url = "https://github.com/GTaherzadeh/SPPPred.git",
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size = "12 pages",
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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