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ME-ACP: Multi-view neural networks with ensemble model for identification of anticancer peptides
Guanwen Feng1, Hang Yao1, Chaoneng Li1
1Xi'an Key Laboratory of Big Data and Intelligent Vision, School of Computer Science and Technology, Xidian University, Xi'an, China.
Abstract:
Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.
Insights
Identifying anticancer peptides (ACPs) is crucial for cancer treatment. ME-ACP, a novel method using multi-view neural networks and ensemble models, accurately identifies ACPs from protein sequences, offering a faster alternative to experiments.
Area of Science:
- Biochemistry
- Bioinformatics
- Computational Biology
Background:
- Cancer is a leading cause of mortality worldwide.
- Anticancer peptides (ACPs) show promise for cancer therapy, offering advantages over traditional treatments.
- Experimental identification of ACPs is costly and time-consuming, necessitating computational approaches.
Purpose of the Study:
- To develop an effective computational method for identifying anticancer peptides (ACPs).
- To propose ME-ACP, a novel approach utilizing multi-view neural networks and ensemble learning for ACP identification.
Main Methods:
- Employed residue and peptide level features using ensemble models based on lightGBMs.
- Integrated lightGBM outputs into a hybrid deep neural network (HDNN) for final ACP classification.
- Validated the ME-ACP method on independent test datasets.
Main Results:
- ME-ACP demonstrated competitive performance on standard evaluation metrics.
- The proposed method effectively identifies anticancer peptides from large protein sequence datasets.
- Achieved high accuracy in distinguishing ACPs from non-ACPs.
Conclusions:
- ME-ACP provides an efficient and accurate computational tool for ACP identification.
- This method can accelerate the discovery of novel anticancer peptides for therapeutic development.
- ME-ACP offers a valuable alternative to traditional experimental methods in anticancer peptide research.
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