Emerging evidence demonstrates that post-translational modification plays an important role in several human complex diseases. Nevertheless, considering the inherent high cost and time consumption of classical and typical in vitro experiments, an increasing attention has been paid to the development of efficient and available computational tools to identify the potential modification sites in the level of protein. In this work, we propose a machine learning-based model called CirBiTree for identification the potential citrullination sites. More specifically, we initially utilize the biprofile Bayesian to extract peptide sequence information. ,en, a flexible neural tree and fuzzy neural network are employed as the classification model. Finally, the most available length of identified peptides has been selected in this model. To evaluate the performance of the proposed methods, some state-of-the-art methods have been employed for comparison. ,e experimental results demonstrate that the proposed method is better than other methods. CirBiTree can achieve 83.07% in sn%, 80.50% in sp, 0.8201 in F1, and 0.6359 in MCC, respectively.

中文翻译：

---

CirBiTree：基于模糊神经网络和灵活神经树的瓜氨酸化位点推断

新出现的证据表明，翻译后修饰在多种人类复杂疾病中起着重要作用。然而，考虑到经典和典型体外实验固有的高成本和时间消耗，人们越来越关注开发有效和可用的计算工具来识别蛋白质水平的潜在修饰位点。在这项工作中，我们提出了一种名为 CirBiTree 的基于机器学习的模型，用于识别潜在的瓜氨酸化位点。更具体地说，我们最初利用双谱贝叶斯来提取肽序列信息。,en，采用灵活的神经树和模糊神经网络作为分类模型。最后，在该模型中选择了最有效长度的已识别肽段。为了评估所提出方法的性能，已采用一些最先进的方法进行比较。,e 实验结果表明，所提出的方法优于其他方法。CirBiTree 可以分别达到 sn% 的 83.07%、sp 的 80.50%、F1 的 0.8201 和 MCC 的 0.6359。