A new local covariance matrix estimation for the classification of gene expression profiles in high dimensional RNA-Seq data
作者：
Highlights：
• A better qtQDA classification method is developed by local dependence function.
• An improved classification method is proposed for personalized medicine.
• Local dependency of genes is modeled.
• Local dependence function is integrated with covariance matrix for significant results.
• Experimental results are performed on two well-known data sets.
摘要
•A better qtQDA classification method is developed by local dependence function.•An improved classification method is proposed for personalized medicine.•Local dependency of genes is modeled.•Local dependence function is integrated with covariance matrix for significant results.•Experimental results are performed on two well-known data sets.
论文关键词：RNA-seq,Gene expression,Local Covariance matrix,Classification,Quadratic Discriminant Analysis
论文评审过程：Received 24 November 2019, Revised 28 September 2020, Accepted 29 October 2020, Available online 2 November 2020, Version of Record 10 February 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2020.114200
