文献类型：期刊文献

英文题名：Grouped gene selection and multi-classification of acute leukemia via new regularized multinomial regression

作者：Li, Juntao[1];Wang, Yanyan[1];Jiang, Tao[2];Xiao, Huimin[2];Song, Xuekun[3]

第一作者：Li, Juntao

通讯作者：Wang, YY[1]

机构：[1]Henan Normal Univ, Sch Math & Informat Sci, Xinxiang 453007, Peoples R China;[2]Henan Univ Econ & Law, Sch Comp & Informat Engn, Zhengzhou 450002, Henan, Peoples R China;[3]Henan Univ Chinese Med, Sch Informat Technol, Zhengzhou 450046, Henan, Peoples R China

第一机构：Henan Normal Univ, Sch Math & Informat Sci, Xinxiang 453007, Peoples R China

通讯机构：[1]corresponding author), Henan Normal Univ, Sch Math & Informat Sci, Xinxiang 453007, Peoples R China.

年份：2018

卷号：667

起止页码：18-24

外文期刊名：GENE

收录：;Scopus(收录号：2-s2.0-85047074921);WOS:【SCI-EXPANDED(收录号:WOS:000436884000003)】；

基金：This work was supported by the Natural Science Foundation of China (61203293, 61702161, 61602153, 61702164), Scientific and Technological Project of Henan Province (172102210047, 162102310461, 172102310535, 182102210020), Natural Science Foundation of Henan Province (162300410184), Foundation of Henan Educational Committee (18A520015, 18A520003, 18B510004), Scientific Research Project of Zhengzhou (153PKJGG128), Foundation for University Young Key Teacher of Henan Province (2016GGJS-079).

语种：英文

外文关键词：Acute leukemia; Multi-classification; Grouped gene selection

摘要：Diagnosing acute leukemia is the necessary prerequisite to treaing it. Multi-classification on the gene expression data of acute leukemia is help for diagnosing it which contains B-cell acute lymphoblastic leukemia (BALL), T cell acute lymphoblastic leukemia (TALL) and acute myeloid leukemia (AML). However, selecting cancer causing genes is a challenging problem in performing multi-classification. In this paper, weighted gene co-expression networks are employed to divide the genes into groups. Based on the dividing groups, a new regularized multinomial regression with overlapping group lasso penalty (MROGL) has been presented to simultaneously perform multi-classification and select gene groups. By implementing this method on three-class acute leukemia data, the grouped genes which work synergistically are identified, and the overlapped genes shared by different groups are also highlighted. Moreover, MROGL outperforms other five methods on multi classification accuracy.