中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 合肥物质科学研究院
  3. 中国科学院合肥物质科学研究院
  4. 中科院合肥智能机械研究所
Improving GRN re-construction by mining hidden regulatory signals

文献类型：期刊论文

作者Shi, Ming1; Shen, Weiming1; Chong, Yanwen1; Wang, Hong-Qiang1,2
刊名IET SYSTEMS BIOLOGY
出版日期2017-12-01
卷号11期号:6页码:174-181
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
DOI10.1049/iet-syb.2017.0013
文献子类Article
英文摘要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.
WOS关键词NETWORK INFERENCE METHODS ; GENE NETWORK ; MUTUAL INFORMATION ; REDUNDANCY REDUCTION ; EXPRESSION DATA ; SPARSE ; MODEL ; ALGORITHM ; ASSOCIATIONS ; DICTIONARY
WOS研究方向Cell Biology ; Mathematical & Computational Biology
语种英语
WOS记录号WOS:000414981900003
资助机构National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; K.C. Wong education foundation ; K.C. Wong education foundation ; K.C. Wong education foundation ; K.C. Wong education foundation ; 61402010) ; 61402010) ; 61402010) ; 61402010) ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; National Natural Science Foundation of China(61374181 ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; Anhui Province Natural Science Foundation(1408085MF133) ; K.C. Wong education foundation ; K.C. Wong education foundation ; K.C. Wong education foundation ; K.C. Wong education foundation ; 61402010) ; 61402010) ; 61402010) ; 61402010)
源URL[http://ir.hfcas.ac.cn:8080/handle/334002/33843]  
专题合肥物质科学研究院_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.

入库方式： OAI收割

来源：合肥物质科学研究院

浏览0
下载0
收藏0
其他版本

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。