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access icon openaccess Improving GRN re-construction by mining hidden regulatory signals

  • Author(s): Ming Shi 1 ; Weiming Shen 1 ; Yanwen Chong 1 ; Hong-Qiang Wang 1, 2
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  • Source: Volume 11, Issue 6, December 2017, p. 174 – 181
    DOI:  10.1049/iet-syb.2017.0013 , Print ISSN 1751-8849, Online ISSN 1751-8857
This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 21/02/2017, Accepted 04/07/2017, Revised 06/06/2017, Published 15/08/2017

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.

Inspec keywords: sensitivity analysis; biology computing; molecular biophysics; genetics; singular value decomposition

Other keywords: dictionary learning model k-SVD; gene regulatory networks; hidden regulatory signal mining; transcription factor; receiver operating characteristic curves; gene expression

Subjects: Biology and medical computing; Molecular biophysics; Physics of subcellular structures; Linear algebra (numerical analysis); Algebra, set theory, and graph theory; Systems theory applications in biology and medicine

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