Zhou Wanyun, Liu Yufei, Li Yingxin, Kong Siqi, Wang Weilin, Ding Boyun, Han Jiyun, Mou Chaozhou, Gao Xin, Liu Juntao
SDU-ANU Joint Science College, Shandong University (Weihai), Weihai 264209, China.
School of Mechanical, Electrical & Information Engineering, Shandong University (Weihai), Weihai 264209, China.
Patterns (N Y). 2023 Feb 28;4(3):100702. doi: 10.1016/j.patter.2023.100702. eCollection 2023 Mar 10.
The accurate identification of anticancer peptides (ACPs) and antimicrobial peptides (AMPs) remains a computational challenge. We propose a tri-fusion neural network termed TriNet for the accurate prediction of both ACPs and AMPs. The framework first defines three kinds of features to capture the peptide information contained in serial fingerprints, sequence evolutions, and physicochemical properties, which are then fed into three parallel modules: a convolutional neural network module enhanced by channel attention, a bidirectional long short-term memory module, and an encoder module for training and final classification. To achieve a better training effect, TriNet is trained via a training approach using iterative interactions between the samples in the training and validation datasets. TriNet is tested on multiple challenging ACP and AMP datasets and exhibits significant improvements over various state-of-the-art methods. The web server and source code of TriNet are respectively available at http://liulab.top/TriNet/server and https://github.com/wanyunzh/TriNet.
准确识别抗癌肽（ACPs）和抗菌肽（AMPs）仍然是一个计算方面的挑战。我们提出了一种名为TriNet的三融合神经网络，用于准确预测ACPs和AMPs。该框架首先定义了三种特征来捕捉序列指纹、序列进化和物理化学性质中包含的肽信息，然后将这些信息输入到三个并行模块中：一个通过通道注意力增强的卷积神经网络模块、一个双向长短期记忆模块以及一个用于训练和最终分类的编码器模块。为了获得更好的训练效果，TriNet通过一种在训练和验证数据集中的样本之间进行迭代交互的训练方法进行训练。TriNet在多个具有挑战性的ACP和AMP数据集上进行了测试，并且相对于各种现有最先进方法都有显著改进。TriNet的网络服务器和源代码分别可在http://liulab.top/TriNet/server和https://github.com/wanyunzh/TriNet获取。
