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To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification
Majed Alsanea1, Abdulsalam S Dukyil2, Afnan3
1Computing Department, Arabeast College, Riyadh 13544, Saudi Arabia.
Abstract:
In the modern technological era, Anti-cancer peptides (ACPs) have been considered a promising cancer treatment. It's critical to find new ACPs to ensure a better knowledge of their functioning processes and vaccine development. Thus, timely and efficient ACPs using a computational technique are highly needed because of the enormous peptide sequences generated in the post-genomic era. Recently, numerous adaptive statistical algorithms have been developed for separating ACPs and NACPs. Despite great advancements, existing approaches still have insufficient feature descriptors and learning methods, limiting predictive performance. To address this, a trustworthy framework is developed for the precise identification of ACPs. Particularly, the presented approach incorporates four hypothetical feature encoding mechanisms namely: amino acid, dipeptide, tripeptide, and an improved version of pseudo amino acid composition are applied to indicate the motif of the target class. Moreover, principal component analysis (PCA) is employed for feature pruning, while selecting optimal, deep, and highly variated features. Due to the diverse nature of learning, experiments are performed over numerous algorithms to select the optimum operating method. After investigating the empirical outcomes, the support vector machine with hybrid feature space shows better performance. The proposed framework achieved an accuracy of 97.09% and 98.25% over the benchmark and independent datasets, respectively. The comparative analysis demonstrates that our proposed model outperforms as compared to the existing methods and is beneficial in drug development, and oncology.
Insights
This study introduces a computational framework for identifying anti-cancer peptides (ACPs). The new method enhances accuracy in detecting ACPs, aiding cancer treatment and vaccine development.
Area of Science:
- Biochemistry and Bioinformatics
- Computational Biology
- Oncology
Background:
- Anti-cancer peptides (ACPs) show promise for cancer therapy, but efficient identification methods are needed.
- Existing computational approaches for ACP identification face limitations in feature descriptors and learning methods.
- The post-genomic era generates vast peptide sequences, necessitating advanced computational tools for ACP discovery.
Purpose of the Study:
- To develop a robust computational framework for precise identification of anti-cancer peptides (ACPs).
- To improve the prediction accuracy of ACPs compared to existing methods.
- To facilitate the discovery of novel ACPs for cancer treatment and vaccine development.
Main Methods:
- Incorporation of four feature encoding mechanisms: amino acid, dipeptide, tripeptide, and pseudo amino acid composition.
- Application of Principal Component Analysis (PCA) for feature selection and dimensionality reduction.
- Utilizing various machine learning algorithms, with Support Vector Machine (SVM) demonstrating superior performance with a hybrid feature space.
Main Results:
- The proposed framework achieved high prediction accuracy: 97.09% on benchmark datasets and 98.25% on independent datasets.
- The hybrid feature space combined with SVM outperformed existing methods in ACP identification.
- The developed model demonstrates significant potential for advancing drug development and oncology research.
Conclusions:
- The novel computational framework offers a significant advancement in the accurate and efficient identification of anti-cancer peptides.
- The method's high performance suggests its utility in accelerating the discovery of new therapeutic peptides for cancer.
- This approach provides a valuable tool for researchers in drug development and the field of oncology.
