半参数投影非线性回归模型系统的综合分析。

IF 1.2 4区 数学 Q4 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Ao Yuan, Tianmin Wu, Hong-Bin Fang, Ming T Tan
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引用次数: 0

摘要

在综合分析中,经常使用参数或非参数方法。前者易于解释,但不具有鲁棒性;后者具有鲁棒性,但不易于解释不同类型变量之间的关系。为了结合这两种方法的优点和灵活性,本文提出了一种半参数投影非线性回归模型系统进行综合分析,对这些不同类型数据的固有坐标结构进行建模,并构建了一种诊断工具将新受试者分类为病例或对照组。通过仿真研究对该方法的性能进行了评价,并取得了令人满意的结果。然后将该方法应用于the Cancer Genome Atlas研究中的一个真实组学数据进行分析,并与另一种整合分析方法相似性网络融合的结果进行了比较,结果表明本文方法更为合理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrative analysis with a system of semiparametric projection non-linear regression models.

In integrative analysis parametric or nonparametric methods are often used. The former is easier for interpretation but not robust, while the latter is robust but not easy to interpret the relationships among the different types of variables. To combine the advantages of both methods and for flexibility, here a system of semiparametric projection non-linear regression models is proposed for the integrative analysis, to model the innate coordinate structure of these different types of data, and a diagnostic tool is constructed to classify new subjects to the case or control group. Simulation studies are conducted to evaluate the performance of the proposed method, and shows promising results. Then the method is applied to analyze a real omics data from The Cancer Genome Atlas study, compared the results with those from the similarity network fusion, another integrative analysis method, and results from our method are more reasonable.

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来源期刊
International Journal of Biostatistics
International Journal of Biostatistics MATHEMATICAL & COMPUTATIONAL BIOLOGY-STATISTICS & PROBABILITY
CiteScore
2.10
自引率
8.30%
发文量
28
审稿时长
>12 weeks
期刊介绍: The International Journal of Biostatistics (IJB) seeks to publish new biostatistical models and methods, new statistical theory, as well as original applications of statistical methods, for important practical problems arising from the biological, medical, public health, and agricultural sciences with an emphasis on semiparametric methods. Given many alternatives to publish exist within biostatistics, IJB offers a place to publish for research in biostatistics focusing on modern methods, often based on machine-learning and other data-adaptive methodologies, as well as providing a unique reading experience that compels the author to be explicit about the statistical inference problem addressed by the paper. IJB is intended that the journal cover the entire range of biostatistics, from theoretical advances to relevant and sensible translations of a practical problem into a statistical framework. Electronic publication also allows for data and software code to be appended, and opens the door for reproducible research allowing readers to easily replicate analyses described in a paper. Both original research and review articles will be warmly received, as will articles applying sound statistical methods to practical problems.
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