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ACPPfel: Explainable deep ensemble learning for anticancer peptides prediction based on feature optimization
Mingyou Liu1,2, Tao Wu1, Xue Li1,2
1School of Biology and Engineering (School of Health Medicine Modern Industry), Guizhou Medical University, Guiyang, China.
Abstract:
Background: Cancer is a significant global health problem that continues to cause a high number of deaths worldwide. Traditional cancer treatments often come with risks that can compromise the functionality of vital organs. As a potential alternative to these conventional therapies, Anticancer peptides (ACPs) have garnered attention for their small size, high specificity, and reduced toxicity, making them as a promising option for cancer treatments. Methods: However, the process of identifying effective ACPs through wet-lab screening experiments is time-consuming and requires a lot of labor. To overcome this challenge, a deep ensemble learning method is constructed to predict anticancer peptides (ACPs) in this study. To evaluate the reliability of the framework, four different datasets are used in this study for training and testing. During the training process of the model, integration of feature selection methods, feature dimensionality reduction measures, and optimization of the deep ensemble model are carried out. Finally, we explored the interpretability of features that affected the final prediction results and built a web server platform to facilitate anticancer peptides prediction, which can be used by all researchers for further studies. This web server can be accessed at http://lmylab.online:5001/. Results: The result of this study achieves an accuracy rate of 98.53% and an AUC (Area under Curve) value of 0.9972 on the ACPfel dataset, it has improvements on other datasets as well.
Insights
This study introduces a deep ensemble learning method to efficiently predict anticancer peptides (ACPs), offering a faster alternative to traditional screening. The developed model achieves high accuracy, aiding in the discovery of novel cancer treatments.
Area of Science:
- Biotechnology
- Computational Biology
- Oncology
Background:
- Cancer remains a leading cause of global mortality, with conventional treatments posing risks to vital organ function.
- Anticancer peptides (ACPs) show promise as targeted cancer therapies due to their specificity and lower toxicity.
- Identifying effective ACPs via traditional wet-lab methods is labor-intensive and time-consuming.
Purpose of the Study:
- To develop a computational framework for predicting anticancer peptides (ACPs).
- To accelerate the identification of potential ACPs for cancer treatment.
- To provide a user-friendly tool for researchers in the field of anticancer peptide discovery.
Main Methods:
- A deep ensemble learning model was constructed for ACP prediction.
- Feature selection and dimensionality reduction techniques were integrated into the model training.
- The model's performance was validated using four distinct datasets.
- Interpretability analysis was performed to identify key predictive features.
Main Results:
- The deep ensemble model achieved high predictive accuracy, reaching 98.53% accuracy and an AUC of 0.9972 on the ACPfel dataset.
- The framework demonstrated improved performance across multiple datasets.
- A web server was developed to facilitate accessible ACP prediction for the research community.
Conclusions:
- The deep ensemble learning approach provides an efficient and accurate method for predicting anticancer peptides.
- This computational strategy significantly reduces the time and resources required for ACP identification.
- The developed web server offers a valuable resource for advancing anticancer peptide research and drug discovery.
