Improving Identification of Essential Proteins by a Novel Ensemble Method

被引:0
|
作者
Dai, Wei [1 ]
Li, Xia [1 ]
Peng, Wei [1 ,2 ]
Song, Jurong [2 ]
Zhong, Jiancheng [3 ]
Wang, Jianxin [4 ]
机构
[1] Kunming Univ Sci & Technol, Fac Informat Engn & Automat, Kunming 650050, Yunnan, Peoples R China
[2] Kunming Univ Sci & Technol, Comp Technol Applicat Key Lab Yunnan Prov, Kunming 650050, Yunnan, Peoples R China
[3] Hunan Normal Univ, Coll Engn & Design, Changsha 410081, Hunan, Peoples R China
[4] Cent South Univ, Comp Sci, Changsha 410081, Hunan, Peoples R China
来源
BIOINFORMATICS RESEARCH AND APPLICATIONS, ISBRA 2019 | 2019年 / 11490卷
基金
中国国家自然科学基金;
关键词
Essential proteins; Ensemble learning; Machine learning; Tri-ensemble; ESSENTIAL GENES; CENTRALITY;
D O I
10.1007/978-3-030-20242-2_13
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Essential proteins are indispensable for cell survival, and the identification of essential proteins plays a critical role in biological and pharmaceutical design research. Recently, some machine learning methods have been proposed by introducing effective protein features or by employing powerful classifiers. Seldom of them focused on improving the prediction accuracy by designing efficient strategies to ensemble different classifiers. In this work, a novel ensemble learning framework called by Tri-ensemble was proposed to integrate different classifiers, which selected three weak classifiers and trained these classifiers by continually adding the samples that are predicted to have abnormally high or abnormally low properties by the other two classifiers. We applied Tri-ensemble on predicting the essential protein of Yeast and E.coli. The results show that our approach achieves better performance than both individual classifiers and the other ensemble learning methods.
引用
收藏
页码:146 / 155
页数:10
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