Modern Management and Information Technology, College of Arts, Media and Technology, Chiang Mai University, Chiang Mai 50200, Thailand.
Pediatric Translational Research Unit, Department of Pediatrics, Faculty of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand.
Brief Bioinform. 2021 Nov 5;22(6). doi: 10.1093/bib/bbab172.
The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are derived from pathogens and can be used as diagnostic biomarkers for predicting various stages of disease severity as well as being used as IL-6 inhibitors for the suppression of aggressive multi-signaling immune responses. Thus, the accurate identification of IL-6 inducing peptides is of great importance for investigating their mechanism of action as well as for developing diagnostic and immunotherapeutic applications. This study proposes a novel stacking ensemble model (termed StackIL6) for accurately identifying IL-6 inducing peptides. More specifically, StackIL6 was constructed from twelve different feature descriptors derived from three major groups of features (composition-based features, composition-transition-distribution-based features and physicochemical properties-based features) and five popular machine learning algorithms (extremely randomized trees, logistic regression, multi-layer perceptron, support vector machine and random forest). To enhance the utility of baseline models, they were effectively and systematically integrated through a stacking strategy to build the final meta-based model. Extensive benchmarking experiments demonstrated that StackIL6 could achieve significantly better performance than the existing method (IL6PRED) and outperformed its constituent baseline models on both training and independent test datasets, which thereby support its excellent discrimination and generalization abilities. To facilitate easy access to the StackIL6 model, it was established as a freely available web server accessible at http://camt.pythonanywhere.com/StackIL6. It is anticipated that StackIL6 can help to facilitate rapid screening of promising IL-6 inducing peptides for the development of diagnostic and immunotherapeutic applications in the future.
白细胞介素 (IL)-6 的释放受到病原体抗原肽以及激活侵袭性炎症的免疫细胞的刺激。IL-6 诱导肽来源于病原体，可作为预测疾病严重程度各个阶段的诊断生物标志物，并可作为 IL-6 抑制剂，抑制侵袭性多信号免疫反应。因此，准确识别 IL-6 诱导肽对于研究其作用机制以及开发诊断和免疫治疗应用具有重要意义。本研究提出了一种新的堆叠集成模型（称为 StackIL6），用于准确识别 IL-6 诱导肽。更具体地说，StackIL6 是由来自三个主要特征组（基于组成的特征、基于组成-转移-分布的特征和基于物理化学性质的特征）的十二个不同特征描述符和五个流行的机器学习算法（极端随机树、逻辑回归、多层感知机、支持向量机和随机森林）构建的。为了提高基线模型的实用性，通过堆叠策略对它们进行了有效而系统的集成，以构建最终的基于元的模型。广泛的基准测试实验表明，StackIL6 可以比现有方法（IL6PRED）取得显著更好的性能，并且在训练和独立测试数据集上都优于其组成的基线模型，从而支持其出色的区分和泛化能力。为了方便访问 StackIL6 模型，我们将其建立为一个免费的网络服务器，可在 http://camt.pythonanywhere.com/StackIL6 上访问。预计 StackIL6 将有助于未来快速筛选有前途的 IL-6 诱导肽，用于开发诊断和免疫治疗应用。
