中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data

文献类型:期刊论文

作者Shi, Ming1,2,3; Tan, Sheng4; Xie, Xin-Ping5; Li, Ao6; Yang, Wulin7,8; Zhu, Tao2,3; Wang, Hong-Qiang1,7,8
刊名BMC GENOMICS
出版日期2020-10-14
卷号21
ISSN号1471-2164
DOI10.1186/s12864-020-07079-8
通讯作者Zhu, Tao(zhut@ustc.edu.cn) ; Wang, Hong-Qiang(hqwang@ustc.edu)
英文摘要Background Genes are regulated by various types of regulators and most of them are still unknown or unobserved. Current gene regulatory networks (GRNs) reverse engineering methods often neglect the unknown regulators and infer regulatory relationships in a local and sub-optimal manner. Results This paper proposes a global GRNs inference framework based on dictionary learning, named dlGRN. The method intends to learn atomic regulators (ARs) from gene expression data using a modified dictionary learning (DL) algorithm, which reflects the whole gene regulatory system, and predicts the regulation between a known regulator and a target gene in a global regression way. The modified DL algorithm fits the scale-free property of biological network, rendering dlGRN intrinsically discern direct and indirect regulations. Conclusions Extensive experimental results on simulation and real-world data demonstrate the effectiveness and efficiency of dlGRN in reverse engineering GRNs. A novel predicted transcription regulation between a TF TFAP2C and an oncogene EGFR was experimentally verified in lung cancer cells. Furthermore, the real application reveals the prevalence of DNA methylation regulation in gene regulatory system. dlGRN can be a standalone tool for GRN inference for its globalization and robustness.
WOS关键词COMPONENT ANALYSIS ; DNA METHYLATION ; EXPRESSION ; ID2 ; INFERENCE ; RECONSTRUCTION ; DICTIONARY ; PROTEINS ; TARGET ; CANCER
资助项目Key Research and Development Program of China[2016YFC1302305] ; National Natural Science Foundation of China ; Anhui Province's key Research and Development Project[201904a07020092] ; Research Projects of Anhui Provincial Education Department[JD2018JD19]
WOS研究方向Biotechnology & Applied Microbiology ; Genetics & Heredity
语种英语
出版者BMC
WOS记录号WOS:000577486200001
资助机构Key Research and Development Program of China ; National Natural Science Foundation of China ; Anhui Province's key Research and Development Project ; Research Projects of Anhui Provincial Education Department
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/104485]  
专题中国科学院合肥物质科学研究院
通讯作者Zhu, Tao; Wang, Hong-Qiang
作者单位1.Chinese Acad Sci, Hefei Inst Phys Sci, Inst Intelligent Machines, MICB Lab, 350 Shushanghu Rd, Hefei 230031, Anhui, Peoples R China
2.Tsinghua Univ, MOE Key Lab Bioinformat, Div Bioinformat, Beijing 100084, Peoples R China
3.Tsinghua Univ, Ctr Synthet & Syst Biol, TNLIST, Dept Automat, Beijing 100084, Peoples R China
4.Univ Sci & Technol China, CAS Key Lab Innate Immun & Chron Dis, Div Life Sci & Med, 96 Jinzhai Rd, Hefei 230026, Anhui, Peoples R China
5.Anhui Jianzhu Univ, Sch Math & Phys, 856 Jinzhai Rd, Hefei 230022, Anhui, Peoples R China
6.Univ Sci & Technol China, Sch Informat Sci & Technol, 96 Jinzhai Rd, Hefei 230026, Anhui, Peoples R China
7.Chinese Acad Sci, Canc Hosp, Ctr Med Phys & Technol, Hefei Inst Phys Sci, 350 Shushanghu Rd, Hefei 230031, Anhui, Peoples R China
8.Chinese Acad Sci, Anhui Prov Key Lab Med Phys & Technol, Ctr Med Phys & Technol, Hefei Inst Phys Sci, 350 Shushanghu Rd, Hefei 230031, Anhui, Peoples R China
推荐引用方式
GB/T 7714
Shi, Ming,Tan, Sheng,Xie, Xin-Ping,et al. Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data[J]. BMC GENOMICS,2020,21.
APA Shi, Ming.,Tan, Sheng.,Xie, Xin-Ping.,Li, Ao.,Yang, Wulin.,...&Wang, Hong-Qiang.(2020).Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data.BMC GENOMICS,21.
MLA Shi, Ming,et al."Globally learning gene regulatory networks based on hidden atomic regulators from transcriptomic big data".BMC GENOMICS 21(2020).

入库方式: OAI收割

来源:合肥物质科学研究院

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