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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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Liu W, Sun X, Peng L, Zhou L, Lin H, Jiang Y. RWRNET: A Gene Regulatory Network Inference Algorithm Using Random Walk With Restart. Front Genet 2020; 11:591461. [PMID: 33101398 PMCID: PMC7545090 DOI: 10.3389/fgene.2020.591461] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 09/02/2020] [Indexed: 11/30/2022] Open
Abstract
Inferring gene regulatory networks from expression data is essential in identifying complex regulatory relationships among genes and revealing the mechanism of certain diseases. Various computation methods have been developed for inferring gene regulatory networks. However, these methods focus on the local topology of the network rather than on the global topology. From network optimisation standpoint, emphasising the global topology of the network also reduces redundant regulatory relationships. In this study, we propose a novel network inference algorithm using Random Walk with Restart (RWRNET) that combines local and global topology relationships. The method first captures the local topology through three elements of random walk and then combines the local topology with the global topology by Random Walk with Restart. The Markov Blanket discovery algorithm is then used to deal with isolated genes. The proposed method is compared with several state-of-the-art methods on the basis of six benchmark datasets. Experimental results demonstrated the effectiveness of the proposed method.
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Affiliation(s)
- Wei Liu
- School of Computer Science, Xiangtan University, Xiangtan, China.,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Xiangtan, China
| | - Xingen Sun
- School of Computer Science, Xiangtan University, Xiangtan, China
| | - Li Peng
- School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
| | - Lili Zhou
- School of Computer Science, Xiangtan University, Xiangtan, China
| | - Hui Lin
- School of Computer Science, Xiangtan University, Xiangtan, China
| | - Yi Jiang
- School of Computer Science, Xiangtan University, Xiangtan, China
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Zhao Z, Fan X, Yang L, Song J, Fang S, Tu J, Chen M, Li J, Zheng L, Wu F, Zhang D, Ying X, Ji J. Recognition of Lung Adenocarcinoma-specific Gene Pairs Based on Genetic Algorithm and Establishment of a Deep Learning Prediction Model. Comb Chem High Throughput Screen 2019; 22:256-265. [PMID: 31142257 DOI: 10.2174/1386207322666190530102245] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 10/30/2018] [Accepted: 11/14/2018] [Indexed: 11/22/2022]
Abstract
AIM AND OBJECTIVE Lung cancer is a disease with a dismal prognosis and is the major cause of cancer deaths in many countries. Nonetheless, rapid technological developments in genome science guarantees more effective prevention and treatment strategies. MATERIALS AND METHODS In this study, genes were pair-matched and screened for lung adenocarcinomaspecific gene relationships. False positives due to fluctuations in single gene expression were avoided and the stability and accuracy of the results was improved. RESULTS Finally, a deep learning model was constructed with machine learning algorithm to realize the clinical diagnosis of lung adenocarcinoma in patients. CONCLUSION Comparing with the traditional methods which takes ingle gene as a feature, the relative difference between gene pairs is a higher order feature, leverage high-order features to build the model can avoid instability caused by a single gene mutation, making the prediction results more reliable.
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Affiliation(s)
- Zhongwei Zhao
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Xiaoxi Fan
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Lili Yang
- Department of Anesthesiology, Zhejiang University Lishui Hospital, The Fifth Affiliated Hospital of Wenzhou Medical University, Lishui Central Hospital, Lishui, China
| | - Jingjing Song
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Shiji Fang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Jianfei Tu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Minjiang Chen
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Jie Li
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Liyun Zheng
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Fazong Wu
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Dengke Zhang
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Xihui Ying
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
| | - Jiansong Ji
- Key Laboratory of Imaging Diagnosis and Minimally Invasive Intervention Research, Lishui Hospital of Zhejiang University, Lishui 323000, China
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Khalid M, Khan S, Ahmad J, Shaheryar M. Identification of self-regulatory network motifs in reverse engineering gene regulatory networks using microarray gene expression data. IET Syst Biol 2019; 13:55-68. [PMID: 33444479 PMCID: PMC8687352 DOI: 10.1049/iet-syb.2018.5001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 11/01/2018] [Accepted: 12/10/2018] [Indexed: 11/19/2022] Open
Abstract
Gene Regulatory Networks (GRNs) are reconstructed from the microarray gene expression data through diversified computational approaches. This process ensues in symmetric and diagonal interaction of gene pairs that cannot be modelled as direct activation, inhibition, and self-regulatory interactions. The values of gene co-expressions could help in identifying co-regulations among them. The proposed approach aims at computing the differences in variances of co-expressed genes rather than computing differences in values of mean expressions across experimental conditions. It adopts multivariate co-variances using principal component analysis (PCA) to predict an asymmetric and non-diagonal gene interaction matrix, to select only those gene pair interactions that exhibit the maximum variances in gene regulatory expressions. The asymmetric gene regulatory interactions help in identifying the controlling regulatory agents, thus lowering the false positive rate by minimizing the connections between previously unlinked network components. The experimental results on real as well as in silico datasets including time-series RTX therapy, Arabidopsis thaliana, DREAM-3, and DREAM-8 datasets, in comparison with existing state-of-the-art approaches demonstrated the enhanced performance of the proposed approach for predicting positive and negative feedback loops and self-regulatory interactions. The generated GRNs hold the potential in determining the real nature of gene pair regulatory interactions.
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Affiliation(s)
- Mehrosh Khalid
- School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabadPakistan
| | - Sharifullah Khan
- School of Electrical Engineering and Computer ScienceNational University of Sciences and TechnologyIslamabadPakistan
| | - Jamil Ahmad
- Research Centre for Modelling and SimulationNational University of Sciences and TechnologyIslamabadPakistan
| | - Muhammad Shaheryar
- Department of Computer ScienceCapital University of Science and TechnologyIslamabadPakistan
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