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A Novel Method to Efficiently Highlight Nonlinearly Expressed Genes

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成果类型:
期刊论文
作者:
Wang, Qifei;Zhang, Haojian;Liang, Yuqing;Jiang, Heling;Tan, Siqiao;...
通讯作者:
Chen, Yuan
作者机构:
[Zhang, Haojian; Jiang, Heling; Yuan, Zheming; Chen, Yuan; Wang, Qifei; Liang, Yuqing] Hunan Agr Univ, Hunan Engn & Technol Res Ctr Agr Big Data Anal &, Changsha, Peoples R China.
[Tan, Siqiao] Hunan Agr Univ, Sch Informat Sci & Technol, Changsha, Peoples R China.
[Luo, Feng] Clemson Univ, Sch Comp, Clemson, SC USA.
通讯机构:
[Chen, Yuan] H
Hunan Agr Univ, Hunan Engn & Technol Res Ctr Agr Big Data Anal &, Changsha, Peoples R China.
语种:
英文
关键词:
RNA sequencing;Maximal information coefficient;Differential expressed gene;Gene selection;normalized differential correlation
期刊:
Frontiers in Genetics
ISSN:
1664-8021
年:
2020
卷:
10
页码:
488214
基金类别:
This work was supported by the National Natural Science Foundation of China (61701177); Hunan Provincial Natural Science Foundation (2018JJ3225); Science foundation open project of Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization (18KFXM08); and Hunan Provincial Research learning and innovative experimental project for college students (SCX1826).
机构署名:
本校为第一且通讯机构
院系归属:
信息科学技术学院
摘要:
For precision medicine, there is a need to identify genes that accurately distinguish the physiological state or response to a particular therapy, but this can be challenging. Many methods of analyzing differential expression have been established and applied to this problem, such as t-test, edgeR, and DEseq2. A common feature of these methods is their focus on a linear relationship (differential expression) between gene expression and phenotype. However, they may overlook nonlinear relationships due to various factors, such as the degree of disease progression, sex, age, ethnicity, and enviro...

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