Background: Anti-Inflammatory Peptides (AIPs) are potent therapeutic agents for inflammatory
and autoimmune disorders due to their high specificity and minimal toxicity under
normal conditions. Therefore, it is greatly significant and beneficial to identify AIPs for further
discovering novel and efficient AIPs-based therapeutics. Recently, three computational approaches,
which can effectively identify potential AIPs, have been developed based on machine learning
algorithms. However, there are several challenges with the existing three predictors.
Objective: A novel machine learning algorithm needs to be proposed to improve the AIPs prediction
Methods: This study attempts to improve the recognition of AIPs by employing multiple primary
sequence-based feature descriptors and an efficient feature selection strategy. By sorting features
through four enhanced minimal redundancy maximal relevance (emRMR) methods, and then attaching
seven different classifiers wrapper methods based on the sequential forward selection algorithm
(SFS), we proposed a hybrid feature selection technique emRMR-SFS to optimize feature
vectors. Furthermore, by evaluating seven classifiers trained with the optimal feature subset, we
developed the Extremely Randomized Tree (ERT) based predictor named PREDAIP for identifying
Results: We systematically compared the performance of PREDAIP with the existing tools on independent
test dataset. It demonstrates the effectiveness and power of the PREDAIP.
Conclusion: The correlation criteria used in emRMR would affect the selection results of the optimal
feature subset at the SFS-wrapper stage, which justifies the necessity for considering different
correlation criteria in emRMR.