特征选择
计算机科学
人工智能
模式识别(心理学)
支持向量机
特征(语言学)
分类
冗余(工程)
选择(遗传算法)
相关性
机器学习
数据挖掘
数学
算法
哲学
几何学
操作系统
语言学
作者
Lin Dan,Yu Jialin,Zhang Ju,He Huan,Guo Xinyun,Shi Shaoping
出处
期刊:Current Bioinformatics
[Bentham Science]
日期:2021-11-09
卷期号:16 (8): 1048-1059
被引量:5
标识
DOI:10.2174/1574893616666210601111157
摘要
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 accuracy. 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 AIPs. 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.
科研通智能强力驱动
Strongly Powered by AbleSci AI