Performance comparison of different ensemble learning strategies with 10-fold cross-validation.

Performance comparison of different ensemble learning strategies with 10-fold cross-validation.

<p>Performance comparison of different ensemble learning strategies with 10-fold cross-validation.</p>

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Bibliographic Details
Main Author:	Chuanmei Bi (20513819) (author)
Other Authors:	Yong Shi (306872) (author), Junfeng Xia (192875) (author), Zhen Liang (659089) (author), Zhiqiang Wu (102384) (author), Kai Xu (122914) (author), Na Cheng (1537555) (author)
Published:	2025
Subjects:	Genetics Virology Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified optimizes feature selection independent test dataset including dna shape friendly web server chemical molecule represents yet current methods driver synonymous mutation enhance predictive accuracy dna shape features driver synonymous mutations dna sequence representations div >< p generate effect scores deep learning methods synonymous mutations deep learning effect scores art methods significantly enhance ensemble learning derived features based features pioneering effect effect predictor significant implications significant correlation shown promise prediction model physicochemical properties initially investigate human cancers hot encoding facilitate researchers existing state data limitations considered neutral bio /</ based predictor based models analysis reveals allowing users
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Summary:	<p>Performance comparison of different ensemble learning strategies with 10-fold cross-validation.</p>