HFCAS OpenIR  > 中科院合肥智能机械研究所
Improving GRN re-construction by mining hidden regulatory signals
Shi, Ming1; Shen, Weiming1; Chong, Yanwen1; Wang, Hong-Qiang1,2
2017-12-01
发表期刊IET SYSTEMS BIOLOGY
卷号11期号:6页码:174-181
摘要Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators. Specifically, the authors modify the dictionary learning model, k-SVD, according to the sparse property of GRNs for mining the regulatory signals. The recovered regulatory signals are then used as a pool of regulatory factors to calculate a confidence score for a given transcription factor regulating a target gene. The capability of recovering hidden regulatory signals was verified on simulated data. Comparative experiments for GRN inference between the proposed algorithm (OURM) and some state-of-the-art algorithms, e.g. GENIE3 and ARACNE, on real-world data sets show the superior performance of OURM in inferring GRNs: higher area under the receiver operating characteristic curves and area under the precision-recall curves.
文章类型Article
关键词Genetics Molecular Biophysics Biology Computing Singular Value Decomposition Sensitivity Analysis Gene Regulatory Networks Gene Expression Hidden Regulatory Signal Mining Transcription Factor Dictionary Learning Model K-svd Receiver Operating Characteristic Curves
WOS标题词Science & Technology ; Life Sciences & Biomedicine
资助者National Natural Science Foundation of China(61374181 ; Anhui Province Natural Science Foundation(1408085MF133) ; K.C. Wong education foundation ; 61402010) ; National Natural Science Foundation of China(61374181 ; Anhui Province Natural Science Foundation(1408085MF133) ; K.C. Wong education foundation ; 61402010)
DOI10.1049/iet-syb.2017.0013
关键词[WOS]NETWORK INFERENCE METHODS ; GENE NETWORK ; MUTUAL INFORMATION ; REDUNDANCY REDUCTION ; EXPRESSION DATA ; SPARSE ; MODEL ; ALGORITHM ; ASSOCIATIONS ; DICTIONARY
收录类别SCI
语种英语
资助者National Natural Science Foundation of China(61374181 ; Anhui Province Natural Science Foundation(1408085MF133) ; K.C. Wong education foundation ; 61402010) ; National Natural Science Foundation of China(61374181 ; Anhui Province Natural Science Foundation(1408085MF133) ; K.C. Wong education foundation ; 61402010)
WOS研究方向Cell Biology ; Mathematical & Computational Biology
WOS类目Cell Biology ; Mathematical & Computational Biology
WOS记录号WOS:000414981900003
引用统计
文献类型期刊论文
条目标识符http://ir.hfcas.ac.cn:8080/handle/334002/33843
专题中科院合肥智能机械研究所
作者单位1.Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China
2.Chinese Acad Sci, Inst Intelligent Machines, Machine Intelligence & Computat Biol Lab, POB 1130, Hefei 230031, Anhui, Peoples R China
推荐引用方式
GB/T 7714
Shi, Ming,Shen, Weiming,Chong, Yanwen,et al. Improving GRN re-construction by mining hidden regulatory signals[J]. IET SYSTEMS BIOLOGY,2017,11(6):174-181.
APA Shi, Ming,Shen, Weiming,Chong, Yanwen,&Wang, Hong-Qiang.(2017).Improving GRN re-construction by mining hidden regulatory signals.IET SYSTEMS BIOLOGY,11(6),174-181.
MLA Shi, Ming,et al."Improving GRN re-construction by mining hidden regulatory signals".IET SYSTEMS BIOLOGY 11.6(2017):174-181.
条目包含的文件 下载所有文件
文件名称/大小 文献类型 版本类型 开放类型 使用许可
期刊论文作者接受稿开放获取CC BY-NC-SA浏览 下载
个性服务
推荐该条目
保存到收藏夹
查看访问统计
导出为Endnote文件
谷歌学术
谷歌学术中相似的文章
[Shi, Ming]的文章
[Shen, Weiming]的文章
[Chong, Yanwen]的文章
百度学术
百度学术中相似的文章
[Shi, Ming]的文章
[Shen, Weiming]的文章
[Chong, Yanwen]的文章
必应学术
必应学术中相似的文章
[Shi, Ming]的文章
[Shen, Weiming]的文章
[Chong, Yanwen]的文章
相关权益政策
暂无数据
收藏/分享
文件名: Improving GRN re-construction by mining hidden regulatory signals.pdf
格式: Adobe PDF
所有评论 (0)
暂无评论
 

除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。