Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh.
Anal Biochem. 2022 Aug 1;650:114707. doi: 10.1016/j.ab.2022.114707. Epub 2022 May 12.
Cancer is one of the most dangerous diseases in the world that often leads to misery and death. Current treatments include different kinds of anticancer therapy which exhibit different types of side effects. Because of certain physicochemical properties, anticancer peptides (ACPs) have opened a new path of treatments for this deadly disease. That is why a well-performed methodology for identifying novel anticancer peptides has great importance in the fight against cancer. In addition to the laboratory techniques, various machine learning and deep learning methodologies have developed in recent years for this task. Although these models have shown reasonable predictive ability, there's still room for improvement in terms of performance and exploring new types of algorithms. In this work, we have proposed a novel multi-channel convolutional neural network (CNN) for identifying anticancer peptides from protein sequences. We have collected data from the existing state-of-the-art methodologies and applied binary encoding for data preprocessing. We have also employed k-fold cross-validation to train our models on benchmark datasets and compared our models' performance on the independent datasets. The comparison has indicated our models' superiority on various evaluation metrics. We think our work can be a valuable asset in finding novel anticancer peptides. We have provided a user-friendly web server for academic purposes and it is publicly available at: http://103.99.176.239/iacp-cnn/.
癌症是世界上最危险的疾病之一，常常导致痛苦和死亡。目前的治疗方法包括各种抗癌疗法，这些疗法表现出不同类型的副作用。由于某些物理化学性质，抗癌肽（ACPs）为治疗这种致命疾病开辟了新的途径。因此，开发一种性能良好的识别新抗癌肽的方法对于抗击癌症具有重要意义。除了实验室技术外，近年来还开发了各种机器学习和深度学习方法来完成这项任务。尽管这些模型已经表现出了合理的预测能力，但在性能和探索新型算法方面仍有改进的空间。在这项工作中，我们提出了一种新的多通道卷积神经网络（CNN），用于从蛋白质序列中识别抗癌肽。我们从现有的最先进的方法中收集了数据，并对数据进行了二进制编码预处理。我们还采用了 k 折交叉验证来在基准数据集上训练我们的模型，并在独立数据集上比较我们模型的性能。比较表明我们的模型在各种评估指标上具有优越性。我们认为我们的工作可以为寻找新的抗癌肽提供有价值的资源。我们为学术目的提供了一个用户友好的网络服务器，它可以在 http://103.99.176.239/iacp-cnn/ 上公开访问。
