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ACP-GBDT: An improved anticancer peptide identification method with gradient boosting decision tree
Yanjuan Li1, Di Ma2, Dong Chen1
1College of Electrical and Information Engineering, Quzhou University, Quzhou, China.
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.
Insights
Identifying anticancer peptides is crucial for cancer treatment. A new predictor, ACP-GBDT, effectively distinguishes anticancer peptides using gradient boosting decision trees and sequence information, offering a simpler and more effective method.
Area of Science:
- Biochemistry
- Bioinformatics
- Computational Biology
Background:
- Cancer remains a leading global cause of mortality, necessitating novel therapeutic strategies.
- Anticancer peptides (ACPs) offer a promising avenue for cancer treatment due to their low side effect profiles.
- Accurate identification of ACPs is a critical research focus for drug development.
Purpose of the Study:
- To develop an improved computational tool for predicting anticancer peptides.
- To enhance the accuracy and efficiency of anticancer peptide identification.
Main Methods:
- Proposed ACP-GBDT, a novel predictor utilizing gradient boosting decision tree (GBDT).
- Employed a merged-feature encoding strategy combining AAIndex and SVMProt-188D for peptide sequence representation.
- Trained the prediction model using GBDT on a curated anticancer peptide dataset.
Main Results:
- ACP-GBDT demonstrated high effectiveness in distinguishing anticancer peptides from non-anticancer ones via independent testing and ten-fold cross-validation.
- Comparative analysis on benchmark datasets indicated that ACP-GBDT outperforms existing prediction methods in terms of simplicity and efficacy.
Conclusions:
- ACP-GBDT represents a significant advancement in computational prediction of anticancer peptides.
- The developed method offers a more effective and simpler alternative for identifying potential anticancer peptide therapeutics.
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