Inflammation is a biological response to harmful stimuli, playing a crucial role in facilitating tissue repair by eradicating pathogenic microorganisms. However, when inflammation becomes chronic, it leads to numerous serious disorders, particularly in autoimmune diseases. Anti-inflammatory peptides (AIPs) have emerged as promising therapeutic agents due to their high specificity, potency, and low toxicity. However, identifying AIPs using traditional in vivo methods is time-consuming and expensive. Recent advancements in computational-based intelligent models for peptides have offered a cost-effective alternative for identifying various inflammatory diseases, owing to their selectivity toward targeted cells with low side effects. In this paper, we propose a novel computational model, namely, DeepAIPs-Pred, for the accurate prediction of AIP sequences. The training samples are represented using LBP-PSSM- and LBP-SMR-based evolutionary image transformation methods. Additionally, to capture contextual semantic features, we employed attention-based ProtBERT-BFD embedding and QLC for structural features. Furthermore, differential evolution (DE)-based weighted feature integration is utilized to produce a multiview feature vector. The SMOTE-Tomek Links are introduced to address the class imbalance problem, and a two-layer feature selection technique is proposed to reduce and select the optimal features. Finally, the novel self-normalized bidirectional temporal convolutional networks (SnBiTCN) are trained using optimal features, achieving a significant predictive accuracy of 94.92% and an AUC of 0.97. The generalization of our proposed model is validated using two independent datasets, demonstrating higher performance with the improvement of ∼2 and ∼10% of accuracies than the existing state-of-the-art model using Ind-I and Ind-II, respectively. The efficacy and reliability of DeepAIPs-Pred highlight its potential as a valuable and promising tool for drug development and research academia.

中文翻译：

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DeepAIPs-Pred：使用局部进化转化图像和基于结构嵌入的最优描述符和自归一化 BiTCN 预测抗炎肽。


炎症是对有害刺激的生物反应，通过根除病原微生物在促进组织修复方面起着至关重要的作用。然而，当炎症变成慢性时，它会导致许多严重的疾病，尤其是在自身免疫性疾病中。抗炎肽 （AIP） 因其高特异性、效力和低毒性而成为有前途的治疗剂。然而，使用传统的体内方法鉴定 AIP 既耗时又昂贵。基于计算的肽智能模型的最新进展为识别各种炎症性疾病提供了一种经济高效的替代方案，因为它们对目标细胞具有低副作用的选择性。在本文中，我们提出了一种新的计算模型，即 DeepAIPs-Pred，用于准确预测 AIP 序列。训练样本使用基于 LBP-PSSM 和 LBP-SMR 的进化图像转换方法表示。此外，为了捕获上下文语义特征，我们采用了基于注意力的 ProtBERT-BFD 嵌入和 QLC 来构建结构特征。此外，利用基于差分进化 （DE） 的加权特征集成来生成多视图特征向量。针对类不平衡问题，引入 SMOTE-Tomek 链接，提出一种两层特征选择技术来归约和选择最优特征。最后，使用最优特征训练新型自归一化双向时间卷积网络 （SnBiTCN），实现了 94.92% 的显着预测准确率和 0.97 的 AUC。 我们提出的模型的泛化使用两个独立的数据集进行了验证，与使用 Ind-I 和 Ind-II 的现有最先进模型相比，分别提高了 ∼2% 和 ∼10% 的精度，证明了更高的性能。DeepAIPs-Pred 的有效性和可靠性凸显了其作为药物开发和研究学术界有价值且有前途的工具的潜力。