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Improving GRN re-construction by mining hidden regulatory signals
Shi, Ming1; Shen, Weiming1; Chong, Yanwen1; Wang, Hong-Qiang1,2
2017-12-01
Source PublicationIET SYSTEMS BIOLOGY
Volume11Issue:6Pages:174-181
AbstractInferring 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.
SubtypeArticle
KeywordGenetics 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 HeadingsScience & Technology ; Life Sciences & Biomedicine
Funding OrganizationNational 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
WOS KeywordNETWORK INFERENCE METHODS ; GENE NETWORK ; MUTUAL INFORMATION ; REDUNDANCY REDUCTION ; EXPRESSION DATA ; SPARSE ; MODEL ; ALGORITHM ; ASSOCIATIONS ; DICTIONARY
Indexed BySCI
Language英语
Funding OrganizationNational 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 Research AreaCell Biology ; Mathematical & Computational Biology
WOS SubjectCell Biology ; Mathematical & Computational Biology
WOS IDWOS:000414981900003
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Document Type期刊论文
Identifierhttp://ir.hfcas.ac.cn:8080/handle/334002/33843
Collection中科院合肥智能机械研究所
Affiliation1.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
Recommended Citation
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.
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