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Peng H, Xu J, Liu K, Liu F, Zhang A, Zhang X. EIEPCF: accurate inference of functional gene regulatory networks by eliminating indirect effects from confounding factors. Brief Funct Genomics 2024; 23:373-383. [PMID: 37642217 DOI: 10.1093/bfgp/elad040] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 07/07/2023] [Accepted: 08/14/2023] [Indexed: 08/31/2023] Open
Abstract
Reconstructing functional gene regulatory networks (GRNs) is a primary prerequisite for understanding pathogenic mechanisms and curing diseases in animals, and it also provides an important foundation for cultivating vegetable and fruit varieties that are resistant to diseases and corrosion in plants. Many computational methods have been developed to infer GRNs, but most of the regulatory relationships between genes obtained by these methods are biased. Eliminating indirect effects in GRNs remains a significant challenge for researchers. In this work, we propose a novel approach for inferring functional GRNs, named EIEPCF (eliminating indirect effects produced by confounding factors), which eliminates indirect effects caused by confounding factors. This method eliminates the influence of confounding factors on regulatory factors and target genes by measuring the similarity between their residuals. The validation results of the EIEPCF method on simulation studies, the gold-standard networks provided by the DREAM3 Challenge and the real gene networks of Escherichia coli demonstrate that it achieves significantly higher accuracy compared to other popular computational methods for inferring GRNs. As a case study, we utilized the EIEPCF method to reconstruct the cold-resistant specific GRN from gene expression data of cold-resistant in Arabidopsis thaliana. The source code and data are available at https://github.com/zhanglab-wbgcas/EIEPCF.
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Affiliation(s)
- Huixiang Peng
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Jing Xu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Kangchen Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- University of Chinese Academy of Sciences, Beijing 100049 China
| | - Fang Liu
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
| | - Aidi Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
| | - Xiujun Zhang
- Key Laboratory of Plant Germplasm Enhancement and Specialty Agriculture, Wuhan Botanical Garden, Chinese Academy of Sciences, Wuhan 430074 China
- Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Wuhan 430074, China
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2
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Gamage HN, Chetty M, Shatte A, Hallinan J. Filter feature selection based Boolean Modelling for Genetic Network Inference. Biosystems 2022; 221:104757. [PMID: 36007675 DOI: 10.1016/j.biosystems.2022.104757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Revised: 08/04/2022] [Accepted: 08/04/2022] [Indexed: 11/02/2022]
Abstract
The reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is highly relevant for the discovery of complex biological interactions and dynamics. Various computational strategies have been developed for this task, but most approaches have low computational efficiency and are not able to cope with high-dimensional, low sample-number, gene expression data. In this paper, we introduce a novel combined filter feature selection approach for efficient and accurate inference of GRNs. A Boolean framework for network modelling is used to demonstrate the efficacy of the proposed approach. Using discretized microarray expression data, the genes most relevant to each target gene are first filtered using ReliefF, an instance-based feature ranking method that is here applied for the first time to GRN inference. Then, further gene selection from the filtered-gene list is done using a mutual information-based min-redundancy max-relevance criterion by eliminating irrelevant genes. This combined method is executed on resampled datasets to finalize the optimal set of regulatory genes. Building upon our previous research, a Pearson correlation coefficient-based Boolean modelling approach is utilized for the efficient identification of the optimal regulatory rules associated with selected regulatory genes. The proposed approach was evaluated using gene expression datasets from small-scale and medium-scale real gene networks, and was observed to be more effective than Linear Discriminant Analysis, performed better than the individual feature selection methods, and obtained improved Structural Accuracy with a higher number of true positives than other state-of-the-art methods, while outperforming these methods with respect to Dynamic Accuracy and efficiency.
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Affiliation(s)
| | - Madhu Chetty
- Health Innovation and Transformation Centre, Federation University, Victoria, Australia
| | - Adrian Shatte
- Health Innovation and Transformation Centre, Federation University, Victoria, Australia
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3
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Liu W, Sun X, Yang L, Li K, Yang Y, Fu X. NSCGRN: a network structure control method for gene regulatory network inference. Brief Bioinform 2022; 23:6585392. [PMID: 35554485 DOI: 10.1093/bib/bbac156] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/27/2022] [Accepted: 04/06/2022] [Indexed: 01/18/2023] Open
Abstract
Accurate inference of gene regulatory networks (GRNs) is an essential premise for understanding pathogenesis and curing diseases. Various computational methods have been developed for GRN inference, but the identification of redundant regulation remains a challenge faced by researchers. Although combining global and local topology can identify and reduce redundant regulations, the topologies' specific forms and cooperation modes are unclear and real regulations may be sacrificed. Here, we propose a network structure control method [network-structure-controlling-based GRN inference method (NSCGRN)] that stipulates the global and local topology's specific forms and cooperation mode. The method is carried out in a cooperative mode of 'global topology dominates and local topology refines'. Global topology requires layering and sparseness of the network, and local topology requires consistency of the subgraph association pattern with the network motifs (fan-in, fan-out, cascade and feedforward loop). Specifically, an ordered gene list is obtained by network topology centrality sorting. A Bernaola-Galvan mutation detection algorithm applied to the list gives the hierarchy of GRNs to control the upstream and downstream regulations within the global scope. Finally, four network motifs are integrated into the hierarchy to optimize local complex regulations and form a cooperative mode where global and local topologies play the dominant and refined roles, respectively. NSCGRN is compared with state-of-the-art methods on three different datasets (six networks in total), and it achieves the highest F1 and Matthews correlation coefficient. Experimental results show its unique advantages in GRN inference.
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Affiliation(s)
- Wei Liu
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China.,School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Xingen Sun
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Li Yang
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Kaiwen Li
- Artificial Intelligence Research Institute, China University of Mining and Technology, Xuzhou, 221116, China
| | - Yu Yang
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Xiangzheng Fu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410000, China
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4
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Liu W, Jiang Y, Peng L, Sun X, Gan W, Zhao Q, Tang H. Inferring Gene Regulatory Networks Using the Improved Markov Blanket Discovery Algorithm. Interdiscip Sci 2021; 14:168-181. [PMID: 34495484 DOI: 10.1007/s12539-021-00478-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 08/22/2021] [Accepted: 08/24/2021] [Indexed: 11/26/2022]
Abstract
Inferring gene regulatory networks (GRNs) from microarray data can help us understand the mechanisms of life and eventually develop effective therapies. Currently, many computational methods have been used in inferring GRNs. However, owing to high-dimensional data and small samples, these methods often tend to introduce redundant regulatory relationships. Therefore, a novel network inference method based on the improved Markov blanket discovery algorithm, IMBDANET, is proposed to infer GRNs. Specifically, for each target gene, data processing inequality was applied to the Markov blanket discovery algorithm for the accurate differentiation of direct regulatory genes from indirect regulatory genes. Finally, direct regulatory genes were used in constructing GRNs, and the network structure was optimized according to the importance degree score. Experimental results on six public network datasets show that the proposed method can be effectively used to infer GRNs.
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Affiliation(s)
- Wei Liu
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
- Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, 411105, China
| | - Yi Jiang
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
| | - Xingen Sun
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Wenqing Gan
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China
| | - Qi Zhao
- School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China.
| | - Huanrong Tang
- School of Computer Science, Xiangtan University, Xiangtan, 411105, China.
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Yu B, Xu JM, Li S, Chen C, Chen RX, Wang L, Zhang Y, Wang MH. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method. Oncotarget 2017; 8:80373-80392. [PMID: 29113310 PMCID: PMC5655205 DOI: 10.18632/oncotarget.21268] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2017] [Accepted: 08/27/2017] [Indexed: 01/31/2023] Open
Abstract
Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli, and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.
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Affiliation(s)
- Bin Yu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- CAS Key Laboratory of Geospace Environment, Department of Geophysics and Planetary Science, University of Science and Technology of China, Hefei 230026, China
- Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Jia-Meng Xu
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Shan Li
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Cheng Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Rui-Xin Chen
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Lei Wang
- Key Laboratory of Eco-chemical Engineering, Ministry of Education, Laboratory of Inorganic Synthesis and Applied Chemistry, College of Chemistry and Molecular Engineering, Qingdao University of Science and Technology, Qingdao 266042, China
| | - Yan Zhang
- Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
- College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266061, China
| | - Ming-Hui Wang
- College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao 266061, China
- Bioinformatics and Systems Biology Research Center, Qingdao University of Science and Technology, Qingdao 266061, China
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6
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Liu W, Zhu W, Liao B, Chen H, Ren S, Cai L. Improving gene regulatory network structure using redundancy reduction in the MRNET algorithm. RSC Adv 2017. [DOI: 10.1039/c7ra01557g] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Inferring gene regulatory networks from expression data is a central problem in systems biology.
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Affiliation(s)
- Wei Liu
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Wen Zhu
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Bo Liao
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Haowen Chen
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Siqi Ren
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
| | - Lijun Cai
- College of Information Science and Engineering
- Hunan University
- Changsha
- China
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