AIEpred: An Ensemble Predictive Model of Classifier Chain to Identify Anti-Inflammatory Peptides

Jinhao Zhang, Zehua Zhang, Lianrong Pu, Jijun Tang, Fei Guo*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Anti-inflammatory peptides (AIEs) have recently emerged as promising therapeutic agent for treatment of various inflammatory diseases, such as rheumatoid arthritis and Alzheimer's disease. Therefore, detecting the correlation between amino acid sequence and its anti-inflammatory property is of great importance for the discovery of new AIEs. To address this issue, we propose a novel prediction tool for accurate identification of peptides as anti-inflammatory epitopes or non anti-inflammatory epitopes. Most of all, we encode the original peptide sequence for better mining and exploring the information and patterns, based on the three feature representations as amino acid contact, position specific scoring matrix, physicochemical property. At the same time, we exploit several feature extraction models and utilize one feature selection model, in order to construct many base classifiers from various feature representations. More specifically, we develop an effective classification model, with which we can extract and learn a set of informative features from the ensemble classifier chain model with different group of base classifiers. Furthermore, in order to test the predictive power of our model, we conduct the comparative experiments on the leave-one-out cross-validation and the independent test. It shows that our novel predictor performs great accurate for identification of AIEs as well as existing outstanding prediction tools. Source codes are available at

Original languageEnglish
Pages (from-to)1831-1840
Number of pages10
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Issue number5
StatePublished - 2021
Externally publishedYes


  • Anti-inflammatory peptides
  • amino acid contact
  • ensemble classifier chain
  • feature extraction
  • feature representation


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