摘要
在生物医学领域,治疗肽作为传统抗生素药物的有效替代品,因其低毒性、高吸收率和高生物活性而被广泛应用于疾病治疗。然而,目前从深度学习的角度预测肽功能的研究还仍有较大改进空间。因此,基于公开的多功能治疗肽数据集,提出了一种基于投影剃度下降的多编码神经网络(PrMFTP-PGD)。首先,结合了多头注意力机制的多编码器提取输入向量的特征并获得较好的表示能力。然后,引入线性注意力机制进一步增强对特征的表示和提取能力。最后,通过投影梯度下降的对抗训练缓解多功能治疗肽数据集中固有的类不平衡问题。在独立测试集上与MPMAB,MLBP,PrMFTP,SP-RNN和ETFC方法进行比较,在精确率、覆盖率、准确率和绝对正确率指标中最大分别提升了2.55%,2.81%,2.59%和2.39%,结果表明,所提方法能够增强模型捕捉序列特征的能力,以更好地对多功能治疗肽进行预测。
Therapeutic peptides are widely used in disease treatment due to their minimal toxicity,high absorption rate and high biological activity as an effective alternative to traditional antibiotic drugs in the field of biomedicine.While there has been limited consideration given to predicting multi-functions of therapeutic peptides in the perspective of deep learning until now.Therefore,a neural network prediction model with projected gradient descent(PGD),called PrMFTP-PGD,is proposed based on publicly available multi-functional therapeutic peptide(MFTP)datasets.The approach involves three steps.First,a multi-encoder is incorporated with a multi-head attention mechanism to extract the features of the input vectors and obtain a better representation capability.Then,a linear attention mechanism is introduced to further enhance the representation and extraction of features.Finally,adversarial training with PGD is used to mitigate the challenges posed by the inherent class imbalance problem in the MFTP datasets for the prediction task.The proposed method is compared with the existing methods,MPMAB,MLBP,PrMFTP and SP-RNN,on an independent test set.It demonstrates the biggest improvements across four key metrics-precision(2.55%),coverage(2.81%),accuracy(2.59%),and absolute correctness(2.39%),indicating that this method can enhance the model’s ability to capture sequence features,so as to better predict multifunctional therapeutic peptides.
作者
冉琴
阮小利
徐婧
李少波
胡丙齐
RAN Qin;RUAN Xiaoli;XU Jing;LI Shaobo;HU Bingqi(State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China)
出处
《计算机科学》
北大核心
2025年第S1期134-139,共6页
Computer Science
基金
贵州省基础研究自然科学项目(ZK[2023]YB054)
贵州省高校人才项目([2022]29)
贵州大学基础研究项目(贵大基础[2024]08号)
国家自然科学基金资助项目(61863005,62163007)。
关键词
多功能治疗肽
功能预测
多标签分类
多编码神经网络
深度学习
Multi-functional therapeutic peptide(MFTP)
Function prediction
Multi-label classification
Multi-coded neural networks
Deep learning
