ArticleFrontiers in genetics2023
ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Meta-Analysis and Topological Perturbation in Interactomic Network for Antiopioid Addiction Drug Repurposing.Journal of chemical information and modeling · 2025Pooled it
- Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local embedded features.BMC biology · 2026Article
- A Scorpion Peptide Exerts Selective Anti-Leukemia Effects Through Disrupting Cell Membranes and Triggering Bax/Bcl-2-Related Apoptosis Pathway.Biomolecules · 2025Article
- AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024Review
- Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis.Research (Washington, D.C.) · 2024Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
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Abstract
Cancer is one of the most dangerous diseases in the world, killing millions of people every year. Drugs composed of anticancer peptides have been used to treat cancer with low side effects in recent years. Therefore, identifying anticancer peptides has become a focus of research. In this study, an improved anticancer peptide predictor named ACP-GBDT, based on gradient boosting decision tree (GBDT) and sequence information, is proposed. To encode the peptide sequences included in the anticancer peptide dataset, ACP-GBDT uses a merged-feature composed of AAIndex and SVMProt-188D. A GBDT is adopted to train the prediction model in ACP-GBDT. Independent testing and ten-fold cross-validation show that ACP-GBDT can effectively distinguish anticancer peptides from non-anticancer ones. The comparison results of the benchmark dataset show that ACP-GBDT is simpler and more effective than other existing anticancer peptide prediction methods.
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