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    政大機構典藏 > 商學院 > 統計學系 > 期刊論文 >  Item 140.119/155774


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    題名: GUEST: an R package for handling estimation of graphical structure and multiclassification for error-prone gene expression data
    作者: 陳立榜
    Chen, Li-Pang;Tsao, Hui-Shan
    貢獻者: 統計系
    日期: 2024-12
    上傳時間: 2025-02-24 15:36:51 (UTC+8)
    摘要: In bioinformatics studies, understanding the network structure of gene expression variables is one of the main interests. In the framework of data science, graphical models have been widely used to characterize the dependence structure among multivariate random variables. However, the gene expression data possibly suffer from ultrahigh-dimensionality and measurement error, which make the detection of network structure challenging and difficult. The other important application of gene expression variables is to provide information to classify subjects into various tumors or diseases. In supervised learning, while linear discriminant analysis is a commonly used approach, the conventional implementation is limited in precisely measured variables and computation of their inverse covariance matrix, which is known as the precision matrix. To tackle those challenges and provide a reliable estimation procedure for public use, we develop the R package GUEST, which is known as Graphical models for Ultrahigh-dimensional and Error-prone data by the booSTing algorithm. This R package aims to deal with measurement error effects in high-dimensional variables under various distributions and then applies the boosting algorithm to identify the network structure and estimate the precision matrix. When the precision matrix is estimated, it can be used to construct the linear discriminant function and improve the accuracy of the classification.
    關聯: Bioinformatics, Vol.40, No.12, btae731
    資料類型: article
    DOI 連結: https://doi.org/10.1093/bioinformatics/btae731
    DOI: 10.1093/bioinformatics/btae731
    顯示於類別:[統計學系] 期刊論文

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