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iACP-MultiCNN: Multi-channel CNN based anticancer peptides identification
Abu Zahid Bin Aziz1, Md Al Mehedi Hasan1, Shamim Ahmad2
1Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Abstract:
Cancer is one of the most dangerous diseases in the world that often leads to misery and death. Current treatments include different kinds of anticancer therapy which exhibit different types of side effects. Because of certain physicochemical properties, anticancer peptides (ACPs) have opened a new path of treatments for this deadly disease. That is why a well-performed methodology for identifying novel anticancer peptides has great importance in the fight against cancer. In addition to the laboratory techniques, various machine learning and deep learning methodologies have developed in recent years for this task. Although these models have shown reasonable predictive ability, there's still room for improvement in terms of performance and exploring new types of algorithms. In this work, we have proposed a novel multi-channel convolutional neural network (CNN) for identifying anticancer peptides from protein sequences. We have collected data from the existing state-of-the-art methodologies and applied binary encoding for data preprocessing. We have also employed k-fold cross-validation to train our models on benchmark datasets and compared our models' performance on the independent datasets. The comparison has indicated our models' superiority on various evaluation metrics. We think our work can be a valuable asset in finding novel anticancer peptides. We have provided a user-friendly web server for academic purposes and it is publicly available at: http://103.99.176.239/iacp-cnn/.
Insights
This study introduces a novel multi-channel convolutional neural network (CNN) for identifying anticancer peptides (ACPs). The developed model demonstrates superior performance in predicting novel ACPs, aiding cancer treatment research.
Area of Science:
- Computational biology
- Bioinformatics
- Cancer research
Background:
- Cancer remains a leading cause of death globally, with current therapies causing significant side effects.
- Anticancer peptides (ACPs) offer a promising therapeutic avenue due to their unique properties.
- Accurate identification of novel ACPs is crucial for advancing cancer treatment strategies.
Purpose of the Study:
- To develop and validate a novel computational method for identifying anticancer peptides (ACPs).
- To improve the predictive performance beyond existing machine learning and deep learning models.
- To provide a user-friendly tool for researchers in the field of anticancer peptide discovery.
Main Methods:
- A novel multi-channel convolutional neural network (CNN) architecture was designed for ACP identification.
- Data from state-of-the-art methods were collected and preprocessed using binary encoding.
- Model training was performed using k-fold cross-validation on benchmark datasets, with performance evaluated on independent datasets.
Main Results:
- The proposed multi-channel CNN model demonstrated superior performance compared to existing methods across various evaluation metrics.
- The model achieved high accuracy in identifying anticancer peptides from protein sequences.
- Comparative analysis confirmed the model's effectiveness on independent test datasets.
Conclusions:
- The developed multi-channel CNN is a valuable tool for the discovery of novel anticancer peptides.
- This computational approach can significantly contribute to the fight against cancer by identifying new therapeutic agents.
- A publicly accessible web server has been developed to facilitate research and academic use.
