Screening Nonlinear miRNA Features of Breast Cancer by Using Ensemble Regularized Polynomial Logistic Regression.

IF 1.4 4区 生物学 Q4 BIOCHEMICAL RESEARCH METHODS
Juntao Li, Shan Xiang, Xuekun Song
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引用次数: 0

Abstract

Differentiating breast cancer subtypes based on miRNA data helps doctors provide more personalized treatment plans for patients. This paper explored the interaction between miRNA pairs and developed a novel ensemble regularized polynomial logistic regression method for screening nonlinear features of breast cancer. Three different types of second-order polynomial logistic regression with elastic network penalty (SOPLR-EN) in which each type contains 10 identical models were integrated to determine the most suitable sample set for feature screening by using bootstrap sampling strategy. A single feature and 39 nonlinear features were obtained by screening features that appeared at least 15 times in 30 integrations and were involved in the classification of at least 4 subtypes. The second-order polynomial logistic regression with ridge penalty (SOPLR-R) built on screened feature set achieved 82.30% classification accuracy for distinguishing breast cancer subtypes, surpassing the performance of other six methods. Further, 11 nonlinear miRNA biomarkers were identified, and their significant relevance to breast cancer was illustrated through six types of biological analysis.

利用集合正则多项式逻辑回归筛选乳腺癌的非线性 miRNA 特征
根据 miRNA 数据区分乳腺癌亚型有助于医生为患者提供更个性化的治疗方案。本文探讨了 miRNA 对之间的相互作用,并开发了一种新型的集合正则化多项式逻辑回归方法,用于筛选乳腺癌的非线性特征。本文整合了三种不同类型的带弹性网络惩罚的二阶多项式逻辑回归(SOPLR-EN),每种类型包含 10 个相同的模型,利用引导取样策略确定最适合特征筛选的样本集。通过筛选在 30 次整合中出现至少 15 次并参与至少 4 个亚型分类的特征,得到了一个单一特征和 39 个非线性特征。基于筛选出的特征集建立的二阶多项式逻辑回归(SOPLR-R)在区分乳腺癌亚型方面达到了 82.30% 的分类准确率,超过了其他六种方法。此外,还发现了 11 个非线性 miRNA 生物标志物,并通过六种生物学分析说明了它们与乳腺癌的重要相关性。
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来源期刊
Journal of Computational Biology
Journal of Computational Biology 生物-计算机:跨学科应用
CiteScore
3.60
自引率
5.90%
发文量
113
审稿时长
6-12 weeks
期刊介绍: Journal of Computational Biology is the leading peer-reviewed journal in computational biology and bioinformatics, publishing in-depth statistical, mathematical, and computational analysis of methods, as well as their practical impact. Available only online, this is an essential journal for scientists and students who want to keep abreast of developments in bioinformatics. Journal of Computational Biology coverage includes: -Genomics -Mathematical modeling and simulation -Distributed and parallel biological computing -Designing biological databases -Pattern matching and pattern detection -Linking disparate databases and data -New tools for computational biology -Relational and object-oriented database technology for bioinformatics -Biological expert system design and use -Reasoning by analogy, hypothesis formation, and testing by machine -Management of biological databases
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