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Updated: Feb 13, 2026
Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Deep capsule neural network for identifying anticancer peptides using sequence to image transformation-based local
Shahid Akbar1,2, Ali Raza3,4,5, Matee Ullah6,5
1Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, Sichuan, China.
Background:
Globally, cancer is a major health issue that poses a significant threat to human health. Traditional treatments and laboratory-based methods have been extensively employed to treat cancer-affected cells. However, their high processing costs and side effects still limit their efficacy. In the past decade, significant developments in the field of anticancer peptides (ACPs) have shown a promising alternative for developing reliable cancer drugs with low side effects.
Results:
In this paper, we presented an effective model, pACP-CapsNet, to accurately identify ACPs. The input sequences are converted into structural and localized substitution-based images using SMR and RECM. Subsequently, HOG, DWT, and CLBP-based transformations are applied to the obtained two-dimensional images to produce novel feature spaces, including RECM_DCT, DWT_SMR, HOG_SMR, and RECM_CLBP. These extracted descriptors are then serially integrated to handle the drawbacks of individual descriptors. Additionally, the shuffled frog leaping algorithm is utilized for selecting the high-ranked features from the integrated hybrid vector. Several deep learning models are trained using SFLA features, among which the Capsule Neural Network (CapsNet) achieved higher prediction rates. The proposed pACP-CapsNet obtained an accuracy of 97.0% and an AUC of 0.98 using training samples. Further validation reveals that pACP-CapsNet outperformed available models, demonstrating improvements of approximately 3% and 4% using the ACP240 and ACP740 test sets, respectively.
Conclusions:
The confirmed efficiency and stability of the pACP-CapsNet model underscore its potential as a valuable tool in academic research, drug diagnosis, and drug design.
Insights
A new model, pACP-CapsNet, accurately identifies anticancer peptides (ACPs) with 97.0% accuracy. This computational tool offers a promising, low-side-effect alternative for cancer drug development.
Area of Science:
- Computational biology
- Bioinformatics
- Drug discovery
Background:
- Cancer remains a significant global health challenge.
- Traditional cancer treatments face limitations due to high costs and side effects.
- Anticancer peptides (ACPs) offer a promising alternative for cancer therapy.
Purpose of the Study:
- To develop an effective computational model for accurate identification of ACPs.
- To leverage deep learning for enhanced prediction of anticancer peptide sequences.
- To address limitations of existing methods in ACP identification.
Main Methods:
- Input sequences were transformed into images using SMR and RECM.
- Feature extraction involved HOG, DWT, and CLBP transformations, creating hybrid feature spaces.
- The shuffled frog leaping algorithm (SFLA) was used for feature selection.
- Capsule Neural Network (CapsNet) was employed for classification.
Main Results:
- The pACP-CapsNet model achieved 97.0% accuracy and 0.98 AUC on training data.
- The model demonstrated superior performance over existing methods on ACP240 and ACP740 test sets.
- Integrated features and SFLA-selected features improved prediction rates.
Conclusions:
- The pACP-CapsNet model shows high efficiency and stability for ACP identification.
- This tool has potential applications in academic research and cancer drug design.
- The model facilitates drug diagnosis and development of novel cancer therapeutics.
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