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Raza K. Fuzzy logic based approaches for gene regulatory network inference. Artif Intell Med 2018; 97:189-203. [PMID: 30573378 DOI: 10.1016/j.artmed.2018.12.004] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 12/10/2018] [Accepted: 12/12/2018] [Indexed: 12/26/2022]
Abstract
The rapid advancements in high-throughput techniques have fueled large-scale production of biological data at very affordable costs. Some of these techniques are microarrays and next-generation sequencing that provide genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, etc., is growing exponentially. These biological data are analyzed using various computational techniques for knowledge discovery - which is also one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays a pivotal role in understanding gene regulation processes and disease mechanism at the molecular level. From last couple of decades, researchers are interested in developing computational algorithms for GRN inference (GRNI) from high-throughput experimental data. Several computational approaches have been proposed for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression-based approaches, probabilistic approaches (Bayesian networks, naïve byes), artificial neural networks and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approaches, is a well-studied technique in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches developed during last two decades for GRNI.
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Affiliation(s)
- Khalid Raza
- Department of Computer Science, Jamia Millia Islamia, New Delhi, India.
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Jia X, Li W, Miao Z, Feng C, Liu Z, He Y, Lv J, Du Y, Hou M, He W, Li D, Chen L. Identification of modules related to programmed cell death in CHD based on EHEN. BIOMED RESEARCH INTERNATIONAL 2014; 2014:475379. [PMID: 25133163 PMCID: PMC4123579 DOI: 10.1155/2014/475379] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Accepted: 05/28/2014] [Indexed: 01/26/2023]
Abstract
The formation and death of macrophages and foam cells are one of the major factors that cause coronary heart disease (CHD). In our study, based on the Edinburgh Human Metabolic Network (EHMN) metabolic network, we built an enzyme network which was constructed by enzymes (nodes) and reactions (edges) called the Edinburgh Human Enzyme Network (EHEN). By integrating the subcellular location information for the reactions and refining the protein-reaction relationships based on the location information, we proposed a computational approach to select modules related to programmed cell death. The identified module was in the EHEN-mitochondria (EHEN-M) and was confirmed to be related to programmed cell death, CHD pathogenesis, and lipid metabolism in the literature. We expected this method could analyze CHD better and more comprehensively from the point of programmed cell death in subnetworks.
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Affiliation(s)
- Xu Jia
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Wan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Zhengqiang Miao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Chenchen Feng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Zhe Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Yuehan He
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Junjie Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Youwen Du
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Min Hou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Weiming He
- Institute of Opto-Electronics, Harbin Institute of Technology, Harbin, Heilongjiang 150000, China
| | - Danbin Li
- Surgery, The First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150000, China
| | - Lina Chen
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150000, China
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