School of Informatics, Xiamen University, Xiamen, Fujian, China.
Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA.
BMC Bioinformatics. 2022 Dec 23;23(Suppl 4):560. doi: 10.1186/s12859-022-04771-2.
Anticancer peptide (ACP) inhibits and kills tumor cells. Research on ACP is of great significance for the development of new drugs, and the prediction of ACPs and non-ACPs is the new hotspot.
We propose a new machine learning-based method named GCNCPR-ACPs (a Graph Convolutional Neural Network Method based on collapse pooling and residual network to predict the ACPs), which automatically and accurately predicts ACPs using residual graph convolution networks, differentiable graph pooling, and features extracted using peptide sequence information extraction. The GCNCPR-ACPs method can effectively capture different levels of node attributes for amino acid node representation learning, GCNCPR-ACPs uses node2vec and one-hot embedding methods to extract initial amino acid features for ACP prediction.
Experimental results of ten-fold cross-validation and independent validation based on different metrics showed that GCNCPR-ACPs significantly outperformed state-of-the-art methods. Specifically, the evaluation indicators of Matthews Correlation Coefficient (MCC) and AUC of our predicator were 69.5% and 90%, respectively, which were 4.3% and 2% higher than those of the other predictors, respectively, in ten-fold cross-validation. And in the independent test, the scores of MCC and SP were 69.6% and 93.9%, respectively, which were 37.6% and 5.5% higher than those of the other predictors, respectively. The overall results showed that the GCNCPR-ACPs method proposed in the current paper can effectively predict ACPs.
抗癌肽（ACP）能抑制和杀伤肿瘤细胞。ACP 的研究对于新药的开发具有重要意义，而 ACP 和非 ACP 的预测是新的热点。
我们提出了一种新的基于机器学习的方法，命名为 GCNCPR-ACPs（基于坍塌池化和残差网络的图卷积神经网络方法来预测 ACP），该方法使用残差图卷积网络、可区分图池化和使用肽序列信息提取的特征，自动且准确地预测 ACP。GCNCPR-ACPs 方法可以有效地捕获节点属性的不同层次，用于氨基酸节点表示学习，GCNCPR-ACPs 使用 node2vec 和 one-hot 嵌入方法提取初始氨基酸特征进行 ACP 预测。
基于不同指标的十折交叉验证和独立验证的实验结果表明，GCNCPR-ACPs 显著优于最先进的方法。具体来说，我们的预测器的马修斯相关系数（MCC）和 AUC 的评估指标分别为 69.5%和 90%，分别比十折交叉验证中其他预测器高 4.3%和 2%。在独立测试中，MCC 和 SP 的得分分别为 69.6%和 93.9%，分别比其他预测器高 37.6%和 5.5%。总体结果表明，本文提出的 GCNCPR-ACPs 方法可以有效地预测 ACP。
