抗癌肽(ACP)的鉴定至关重要,特别是在基于肽的癌症治疗的发展中。诸如分裂氨基酸组成(SAAC)和伪氨基酸组成(PseAAC)的经典模型缺乏特征表示的并入。这些改进提高了ACP识别的预测准确性和效率。因此,这项研究的努力是提出和开发一种基于特征提取的高级框架。因此,为了在本文中实现该目的,我们提出了扩展二肽组合物(EDPC)框架。所提出的EDPC框架通过考虑局部序列环境信息并改革CD-HIT框架以去除噪声和冗余来扩展二肽组成。为了测量准确性,我们做了几个实验。这些实验是使用四种著名的机器学习(ML)算法进行的:支持向量机(SVM),决策树(DT)随机森林(RF),和K近邻(KNN)。为了进行比较,我们使用了准确性,特异性,灵敏度,精度,召回,和F1分数作为评价标准。使用统计显著性检验进一步评估了所提出的框架的可靠性。因此,提出的EDPC框架表现出比SAAC和PseAAC增强的性能,其中SVM模型提供了96的最高精度。6%,特异性显著增强,灵敏度,精度,和多个数据集的F1分数。由于结合了增强的特征表示以及结合了局部和全局序列简档,因此提出的EDPC实现了更高的分类性能。所提出的框架可以处理噪声并且还可以复制特征。这些伴随着广泛的特征表示。最后,我们提出的框架可用于ACP鉴定至关重要的临床应用.未来的工作将包括扩展到更多种类的数据集,结合三级结构信息,并使用深度学习技术来改进所提出的EDPC。
The identification of anticancer peptides (ACPs) is crucial, especially in the development of peptide-based cancer therapy. The classical models such as Split Amino Acid Composition (SAAC) and Pseudo Amino Acid Composition (PseAAC) lack the incorporation of feature representation. These advancements improve the predictive accuracy and efficiency of ACP identification. Thus, the effort of this research is to propose and develop an advanced framework based on feature extraction. Thus, to achieve this objective herein we propose an Extended Dipeptide Composition (EDPC) framework. The proposed EDPC framework extends the dipeptide composition by considering the local sequence environment information and reforming the CD-HIT framework to remove noise and redundancy. To measure the accuracy, we have performed several experiments. These experiments were employed using four famous machine learning (ML) algorithms named; Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K Nearest Neighbor (KNN). For comparisons, we have used accuracy, specificity, sensitivity, precision, recall, and F1-Score as evaluation criteria. The reliability of the proposed framework is further evaluated using statistical significance tests. As a result, the proposed EDPC framework exhibited enhanced performance than SAAC and PseAAC, where the SVM model delivered the highest accuracy of 96. 6% and significant enhancements in specificity, sensitivity, precision, and F1-score over multiple datasets. Due to the incorporation of enhanced feature representation and the incorporation of local and global sequence profiles proposed EDPC achieves higher classification performance. The proposed frameworks can deal with noise and also duplicating features. These are accompanied by a wide range of feature representations. Finally, our proposed framework can be used for clinical applications where ACP identification is essential. Future works will include extending to a larger variety of datasets, incorporating tertiary structural information, and using deep learning techniques to improve the proposed EDPC.