CirBiTree: Citrullination Site Inference Based on a Fuzzy Neural Network and Flexible Neural Tree

摘要: 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 typical vitro experiments, increasing attention has been paid to development efficient available computational tools identify potential sites level protein. In this work, we propose a machine learning-based model called CirBiTree for identification citrullination sites. More specifically, initially utilize biprofile Bayesian extract peptide sequence information. Then, flexible neural tree fuzzy network are employed as classification model. Finally, most length identified peptides selected To evaluate performance proposed methods, some state-of-the-art methods have comparison. The experimental results demonstrate method is better than other methods. can achieve 83.07% sn%, 80.50% sp, 0.8201 F1, 0.6359 MCC, respectively.