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MCLRP: enhanced prediction of anticancer drug response through low-rank matrix completion and transcriptomic profiling.

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第一作者： Kun,Wang

第一单位： School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, China.

作者： Kun,Wang ^[1] ; Binhan,Li ^[1] ; Miao,Xu ^[2] ; Dailin,Ding ^[1] ; Qihui,Zheng ^[1] ; Geng,Tian ^[3] ; Xueying,Zeng ^[4] ; Jialiang,Yang ^[5]

作者单位： School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, China. ^[1] Broad Institute of MIT and Harvard, 415 Main Street, Cambridge, MA, 02142, USA. ^[2] Geneis Beijing Co., Ltd., Beijing, 100102, China. ^[3] School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, China. zxying@ouc.edu.cn.;Laboratory of Marine Mathematics, Ocean University of China, Qingdao, 266100, China. zxying@ouc.edu.cn. ^[4] Geneis Beijing Co., Ltd., Beijing, 100102, China. yangjl@geneis.cn.;Academician Workstation, Changsha Medical University, Changsha, 410219, China. yangjl@geneis.cn. ^[5]

关键词 Drug-gene association networks Gene co-mutations Low-rank matrix completion Model interpretability Principal component analysis

医学主题词抗肿瘤药(Antineoplastic Agents);人类(Humans);基因表达谱(Gene Expression Profiling);细胞系, 肿瘤(Cell Line, Tumor);主成分分析(Principal Component Analysis)

DOI 10.1186/s12915-025-02457-8

PMID 41340127

发布时间 2026-01-10

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