Akbar Shahid, Hayat Maqsood, Iqbal Muhammad, Jan Mian Ahmad
Department of Computer Science, Abdul Wali Khan University Mardan, KP 23200, Pakistan.
Artif Intell Med. 2017 Jun;79:62-70. doi: 10.1016/j.artmed.2017.06.008. Epub 2017 Jun 17.
Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.
癌症是一种致命疾病，在发达国家占所有死亡人数的四分之一。传统的抗癌疗法，如化疗和放疗，成本高昂、容易出错且技术效果不佳。这些传统技术会对人体细胞产生严重的副作用。由于癌症的严重影响，开发一种准确且高效的智能计算模型来识别抗癌肽是很有必要的。在本文中，提出了基于进化智能遗传算法的集成模型“iACP-GAEnsC”来识别抗癌肽。在该模型中，使用三种不同的离散特征表示方法来构建蛋白质序列，即两亲性伪氨基酸组成、g-间隙二肽组成和简化氨基酸字母组成。分别研究提取的特征空间的性能，然后将它们合并以展示杂交的重要性。此外，使用优化的遗传算法和简单多数技术将各个分类器的预测结果组合在一起，以提高真分类率。可以观察到，基于遗传算法的集成分类优于单个分类器以及基于简单多数投票的集成。基于遗传算法的集成分类在混合特征空间上的性能得到了高度报道，准确率为96.45%。与现有技术相比，“iACP-GAEnsC”模型在各种性能指标方面都取得了显著的改进。基于模拟结果，可以观察到“iACP-GAEnsC”模型可能会成为研究人员在药物设计和蛋白质组学领域的领先工具。
